Overview

Brought to you by YData

Dataset statistics

Number of variables50
Number of observations1460
Missing cells6049
Missing cells (%)8.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory570.4 KiB
Average record size in memory400.1 B

Variable types

Numeric18
Categorical31
Boolean1

Alerts

1stFlrSF is highly overall correlated with SalePrice and 1 other fieldsHigh correlation
Alley is highly overall correlated with BldgType and 11 other fieldsHigh correlation
BedroomAbvGr is highly overall correlated with GrLivArea and 1 other fieldsHigh correlation
BldgType is highly overall correlated with Alley and 1 other fieldsHigh correlation
BsmtFinSF1 is highly overall correlated with BsmtUnfSFHigh correlation
BsmtQual is highly overall correlated with Alley and 3 other fieldsHigh correlation
BsmtUnfSF is highly overall correlated with BsmtFinSF1High correlation
ExterQual is highly overall correlated with Alley and 2 other fieldsHigh correlation
Exterior1st is highly overall correlated with Exterior2ndHigh correlation
Exterior2nd is highly overall correlated with Exterior1stHigh correlation
Foundation is highly overall correlated with Alley and 1 other fieldsHigh correlation
FullBath is highly overall correlated with MiscFeatureHigh correlation
GarageArea is highly overall correlated with GarageCars and 3 other fieldsHigh correlation
GarageCars is highly overall correlated with GarageAreaHigh correlation
GarageFinish is highly overall correlated with AlleyHigh correlation
GrLivArea is highly overall correlated with BedroomAbvGr and 3 other fieldsHigh correlation
HalfBath is highly overall correlated with MSSubClassHigh correlation
HouseStyle is highly overall correlated with MSSubClassHigh correlation
KitchenQual is highly overall correlated with ExterQual and 1 other fieldsHigh correlation
LotArea is highly overall correlated with Alley and 1 other fieldsHigh correlation
LotFrontage is highly overall correlated with LotAreaHigh correlation
MSSubClass is highly overall correlated with Alley and 3 other fieldsHigh correlation
MSZoning is highly overall correlated with Alley and 1 other fieldsHigh correlation
MiscFeature is highly overall correlated with Alley and 1 other fieldsHigh correlation
Neighborhood is highly overall correlated with Alley and 2 other fieldsHigh correlation
OverallQual is highly overall correlated with Alley and 8 other fieldsHigh correlation
SalePrice is highly overall correlated with 1stFlrSF and 7 other fieldsHigh correlation
TotRmsAbvGrd is highly overall correlated with BedroomAbvGr and 2 other fieldsHigh correlation
TotalBsmtSF is highly overall correlated with 1stFlrSF and 1 other fieldsHigh correlation
YearBuilt is highly overall correlated with Alley and 6 other fieldsHigh correlation
YearRemodAdd is highly overall correlated with OverallQual and 2 other fieldsHigh correlation
MSZoning is highly imbalanced (56.9%) Imbalance
BldgType is highly imbalanced (59.4%) Imbalance
RoofStyle is highly imbalanced (65.1%) Imbalance
ExterCond is highly imbalanced (72.8%) Imbalance
CentralAir is highly imbalanced (65.3%) Imbalance
KitchenAbvGr is highly imbalanced (85.7%) Imbalance
MiscFeature is highly imbalanced (70.7%) Imbalance
LotFrontage has 259 (17.7%) missing values Missing
Alley has 1369 (93.8%) missing values Missing
MasVnrType has 872 (59.7%) missing values Missing
BsmtQual has 37 (2.5%) missing values Missing
BsmtExposure has 38 (2.6%) missing values Missing
BsmtFinType1 has 37 (2.5%) missing values Missing
FireplaceQu has 690 (47.3%) missing values Missing
GarageType has 81 (5.5%) missing values Missing
GarageFinish has 81 (5.5%) missing values Missing
Fence has 1179 (80.8%) missing values Missing
MiscFeature has 1406 (96.3%) missing values Missing
Id is uniformly distributed Uniform
Id has unique values Unique
BsmtFinSF1 has 467 (32.0%) zeros Zeros
BsmtUnfSF has 118 (8.1%) zeros Zeros
TotalBsmtSF has 37 (2.5%) zeros Zeros
GarageArea has 81 (5.5%) zeros Zeros

Reproduction

Analysis started2025-05-09 18:50:28.959796
Analysis finished2025-05-09 18:52:01.739485
Duration1 minute and 32.78 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Id
Real number (ℝ)

Uniform  Unique 

Distinct1460
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean730.5
Minimum1
Maximum1460
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-05-10T00:22:01.926833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile73.95
Q1365.75
median730.5
Q31095.25
95-th percentile1387.05
Maximum1460
Range1459
Interquartile range (IQR)729.5

Descriptive statistics

Standard deviation421.61001
Coefficient of variation (CV)0.57715265
Kurtosis-1.2
Mean730.5
Median Absolute Deviation (MAD)365
Skewness0
Sum1066530
Variance177755
MonotonicityStrictly increasing
2025-05-10T00:22:02.250785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
982 1
 
0.1%
980 1
 
0.1%
979 1
 
0.1%
978 1
 
0.1%
977 1
 
0.1%
976 1
 
0.1%
975 1
 
0.1%
974 1
 
0.1%
973 1
 
0.1%
Other values (1450) 1450
99.3%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1460 1
0.1%
1459 1
0.1%
1458 1
0.1%
1457 1
0.1%
1456 1
0.1%
1455 1
0.1%
1454 1
0.1%
1453 1
0.1%
1452 1
0.1%
1451 1
0.1%

MSSubClass
Real number (ℝ)

High correlation 

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.89726
Minimum20
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-05-10T00:22:02.506933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q120
median50
Q370
95-th percentile160
Maximum190
Range170
Interquartile range (IQR)50

Descriptive statistics

Standard deviation42.300571
Coefficient of variation (CV)0.74345532
Kurtosis1.580188
Mean56.89726
Median Absolute Deviation (MAD)30
Skewness1.4076567
Sum83070
Variance1789.3383
MonotonicityNot monotonic
2025-05-10T00:22:02.742002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
20 536
36.7%
60 299
20.5%
50 144
 
9.9%
120 87
 
6.0%
30 69
 
4.7%
160 63
 
4.3%
70 60
 
4.1%
80 58
 
4.0%
90 52
 
3.6%
190 30
 
2.1%
Other values (5) 62
 
4.2%
ValueCountFrequency (%)
20 536
36.7%
30 69
 
4.7%
40 4
 
0.3%
45 12
 
0.8%
50 144
 
9.9%
60 299
20.5%
70 60
 
4.1%
75 16
 
1.1%
80 58
 
4.0%
85 20
 
1.4%
ValueCountFrequency (%)
190 30
 
2.1%
180 10
 
0.7%
160 63
 
4.3%
120 87
 
6.0%
90 52
 
3.6%
85 20
 
1.4%
80 58
 
4.0%
75 16
 
1.1%
70 60
 
4.1%
60 299
20.5%

MSZoning
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
RL
1151 
RM
218 
FV
 
65
RH
 
16
C (all)
 
10

Length

Max length7
Median length2
Mean length2.0342466
Min length2

Characters and Unicode

Total characters2970
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRL
2nd rowRL
3rd rowRL
4th rowRL
5th rowRL

Common Values

ValueCountFrequency (%)
RL 1151
78.8%
RM 218
 
14.9%
FV 65
 
4.5%
RH 16
 
1.1%
C (all) 10
 
0.7%

Length

2025-05-10T00:22:03.032677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-10T00:22:03.282252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
rl 1151
78.3%
rm 218
 
14.8%
fv 65
 
4.4%
rh 16
 
1.1%
c 10
 
0.7%
all 10
 
0.7%

Most occurring characters

ValueCountFrequency (%)
R 1385
46.6%
L 1151
38.8%
M 218
 
7.3%
F 65
 
2.2%
V 65
 
2.2%
l 20
 
0.7%
H 16
 
0.5%
C 10
 
0.3%
10
 
0.3%
( 10
 
0.3%
Other values (2) 20
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2970
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 1385
46.6%
L 1151
38.8%
M 218
 
7.3%
F 65
 
2.2%
V 65
 
2.2%
l 20
 
0.7%
H 16
 
0.5%
C 10
 
0.3%
10
 
0.3%
( 10
 
0.3%
Other values (2) 20
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2970
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 1385
46.6%
L 1151
38.8%
M 218
 
7.3%
F 65
 
2.2%
V 65
 
2.2%
l 20
 
0.7%
H 16
 
0.5%
C 10
 
0.3%
10
 
0.3%
( 10
 
0.3%
Other values (2) 20
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2970
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 1385
46.6%
L 1151
38.8%
M 218
 
7.3%
F 65
 
2.2%
V 65
 
2.2%
l 20
 
0.7%
H 16
 
0.5%
C 10
 
0.3%
10
 
0.3%
( 10
 
0.3%
Other values (2) 20
 
0.7%

LotFrontage
Real number (ℝ)

High correlation  Missing 

Distinct110
Distinct (%)9.2%
Missing259
Missing (%)17.7%
Infinite0
Infinite (%)0.0%
Mean70.049958
Minimum21
Maximum313
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-05-10T00:22:03.734364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile34
Q159
median69
Q380
95-th percentile107
Maximum313
Range292
Interquartile range (IQR)21

Descriptive statistics

Standard deviation24.284752
Coefficient of variation (CV)0.3466776
Kurtosis17.452867
Mean70.049958
Median Absolute Deviation (MAD)11
Skewness2.1635691
Sum84130
Variance589.74917
MonotonicityNot monotonic
2025-05-10T00:22:04.022469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 143
 
9.8%
70 70
 
4.8%
80 69
 
4.7%
50 57
 
3.9%
75 53
 
3.6%
65 44
 
3.0%
85 40
 
2.7%
78 25
 
1.7%
90 23
 
1.6%
21 23
 
1.6%
Other values (100) 654
44.8%
(Missing) 259
 
17.7%
ValueCountFrequency (%)
21 23
1.6%
24 19
1.3%
30 6
 
0.4%
32 5
 
0.3%
33 1
 
0.1%
34 10
0.7%
35 9
 
0.6%
36 6
 
0.4%
37 5
 
0.3%
38 1
 
0.1%
ValueCountFrequency (%)
313 2
0.1%
182 1
0.1%
174 2
0.1%
168 1
0.1%
160 1
0.1%
153 1
0.1%
152 1
0.1%
150 1
0.1%
149 1
0.1%
144 1
0.1%

LotArea
Real number (ℝ)

High correlation 

Distinct1073
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10516.828
Minimum1300
Maximum215245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-05-10T00:22:04.342322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1300
5-th percentile3311.7
Q17553.5
median9478.5
Q311601.5
95-th percentile17401.15
Maximum215245
Range213945
Interquartile range (IQR)4048

Descriptive statistics

Standard deviation9981.2649
Coefficient of variation (CV)0.9490756
Kurtosis203.24327
Mean10516.828
Median Absolute Deviation (MAD)1998
Skewness12.207688
Sum15354569
Variance99625650
MonotonicityNot monotonic
2025-05-10T00:22:04.633979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7200 25
 
1.7%
9600 24
 
1.6%
6000 17
 
1.2%
9000 14
 
1.0%
8400 14
 
1.0%
10800 14
 
1.0%
1680 10
 
0.7%
7500 9
 
0.6%
9100 8
 
0.5%
8125 8
 
0.5%
Other values (1063) 1317
90.2%
ValueCountFrequency (%)
1300 1
 
0.1%
1477 1
 
0.1%
1491 1
 
0.1%
1526 1
 
0.1%
1533 2
 
0.1%
1596 1
 
0.1%
1680 10
0.7%
1869 1
 
0.1%
1890 2
 
0.1%
1920 1
 
0.1%
ValueCountFrequency (%)
215245 1
0.1%
164660 1
0.1%
159000 1
0.1%
115149 1
0.1%
70761 1
0.1%
63887 1
0.1%
57200 1
0.1%
53504 1
0.1%
53227 1
0.1%
53107 1
0.1%

Alley
Categorical

High correlation  Missing 

Distinct2
Distinct (%)2.2%
Missing1369
Missing (%)93.8%
Memory size11.5 KiB
Grvl
50 
Pave
41 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters364
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGrvl
2nd rowPave
3rd rowPave
4th rowGrvl
5th rowPave

Common Values

ValueCountFrequency (%)
Grvl 50
 
3.4%
Pave 41
 
2.8%
(Missing) 1369
93.8%

Length

2025-05-10T00:22:04.890546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-10T00:22:05.072867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
grvl 50
54.9%
pave 41
45.1%

Most occurring characters

ValueCountFrequency (%)
v 91
25.0%
G 50
13.7%
r 50
13.7%
l 50
13.7%
P 41
11.3%
a 41
11.3%
e 41
11.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 364
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
v 91
25.0%
G 50
13.7%
r 50
13.7%
l 50
13.7%
P 41
11.3%
a 41
11.3%
e 41
11.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 364
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
v 91
25.0%
G 50
13.7%
r 50
13.7%
l 50
13.7%
P 41
11.3%
a 41
11.3%
e 41
11.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 364
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
v 91
25.0%
G 50
13.7%
r 50
13.7%
l 50
13.7%
P 41
11.3%
a 41
11.3%
e 41
11.3%

LotShape
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Reg
925 
IR1
484 
IR2
 
41
IR3
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4380
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReg
2nd rowReg
3rd rowIR1
4th rowIR1
5th rowIR1

Common Values

ValueCountFrequency (%)
Reg 925
63.4%
IR1 484
33.2%
IR2 41
 
2.8%
IR3 10
 
0.7%

Length

2025-05-10T00:22:05.277812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-10T00:22:05.484824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
reg 925
63.4%
ir1 484
33.2%
ir2 41
 
2.8%
ir3 10
 
0.7%

Most occurring characters

ValueCountFrequency (%)
R 1460
33.3%
e 925
21.1%
g 925
21.1%
I 535
 
12.2%
1 484
 
11.1%
2 41
 
0.9%
3 10
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4380
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 1460
33.3%
e 925
21.1%
g 925
21.1%
I 535
 
12.2%
1 484
 
11.1%
2 41
 
0.9%
3 10
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4380
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 1460
33.3%
e 925
21.1%
g 925
21.1%
I 535
 
12.2%
1 484
 
11.1%
2 41
 
0.9%
3 10
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4380
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 1460
33.3%
e 925
21.1%
g 925
21.1%
I 535
 
12.2%
1 484
 
11.1%
2 41
 
0.9%
3 10
 
0.2%

LotConfig
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Inside
1052 
Corner
263 
CulDSac
 
94
FR2
 
47
FR3
 
4

Length

Max length7
Median length6
Mean length5.959589
Min length3

Characters and Unicode

Total characters8701
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInside
2nd rowFR2
3rd rowInside
4th rowCorner
5th rowFR2

Common Values

ValueCountFrequency (%)
Inside 1052
72.1%
Corner 263
 
18.0%
CulDSac 94
 
6.4%
FR2 47
 
3.2%
FR3 4
 
0.3%

Length

2025-05-10T00:22:05.756341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-10T00:22:05.999666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
inside 1052
72.1%
corner 263
 
18.0%
culdsac 94
 
6.4%
fr2 47
 
3.2%
fr3 4
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e 1315
15.1%
n 1315
15.1%
I 1052
12.1%
s 1052
12.1%
i 1052
12.1%
d 1052
12.1%
r 526
 
6.0%
C 357
 
4.1%
o 263
 
3.0%
S 94
 
1.1%
Other values (9) 623
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8701
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1315
15.1%
n 1315
15.1%
I 1052
12.1%
s 1052
12.1%
i 1052
12.1%
d 1052
12.1%
r 526
 
6.0%
C 357
 
4.1%
o 263
 
3.0%
S 94
 
1.1%
Other values (9) 623
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8701
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1315
15.1%
n 1315
15.1%
I 1052
12.1%
s 1052
12.1%
i 1052
12.1%
d 1052
12.1%
r 526
 
6.0%
C 357
 
4.1%
o 263
 
3.0%
S 94
 
1.1%
Other values (9) 623
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8701
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1315
15.1%
n 1315
15.1%
I 1052
12.1%
s 1052
12.1%
i 1052
12.1%
d 1052
12.1%
r 526
 
6.0%
C 357
 
4.1%
o 263
 
3.0%
S 94
 
1.1%
Other values (9) 623
7.2%

Neighborhood
Categorical

High correlation 

Distinct25
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
NAmes
225 
CollgCr
150 
OldTown
113 
Edwards
100 
Somerst
86 
Other values (20)
786 

Length

Max length7
Median length7
Mean length6.4945205
Min length5

Characters and Unicode

Total characters9482
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCollgCr
2nd rowVeenker
3rd rowCollgCr
4th rowCrawfor
5th rowNoRidge

Common Values

ValueCountFrequency (%)
NAmes 225
15.4%
CollgCr 150
 
10.3%
OldTown 113
 
7.7%
Edwards 100
 
6.8%
Somerst 86
 
5.9%
Gilbert 79
 
5.4%
NridgHt 77
 
5.3%
Sawyer 74
 
5.1%
NWAmes 73
 
5.0%
SawyerW 59
 
4.0%
Other values (15) 424
29.0%

Length

2025-05-10T00:22:06.260635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
names 225
15.4%
collgcr 150
 
10.3%
oldtown 113
 
7.7%
edwards 100
 
6.8%
somerst 86
 
5.9%
gilbert 79
 
5.4%
nridght 77
 
5.3%
sawyer 74
 
5.1%
nwames 73
 
5.0%
sawyerw 59
 
4.0%
Other values (15) 424
29.0%

Most occurring characters

ValueCountFrequency (%)
r 931
 
9.8%
e 905
 
9.5%
l 622
 
6.6%
d 506
 
5.3%
s 486
 
5.1%
o 483
 
5.1%
m 439
 
4.6%
N 425
 
4.5%
w 414
 
4.4%
C 407
 
4.3%
Other values (28) 3864
40.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9482
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 931
 
9.8%
e 905
 
9.5%
l 622
 
6.6%
d 506
 
5.3%
s 486
 
5.1%
o 483
 
5.1%
m 439
 
4.6%
N 425
 
4.5%
w 414
 
4.4%
C 407
 
4.3%
Other values (28) 3864
40.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9482
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 931
 
9.8%
e 905
 
9.5%
l 622
 
6.6%
d 506
 
5.3%
s 486
 
5.1%
o 483
 
5.1%
m 439
 
4.6%
N 425
 
4.5%
w 414
 
4.4%
C 407
 
4.3%
Other values (28) 3864
40.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9482
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 931
 
9.8%
e 905
 
9.5%
l 622
 
6.6%
d 506
 
5.3%
s 486
 
5.1%
o 483
 
5.1%
m 439
 
4.6%
N 425
 
4.5%
w 414
 
4.4%
C 407
 
4.3%
Other values (28) 3864
40.8%

BldgType
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1Fam
1220 
TwnhsE
 
114
Duplex
 
52
Twnhs
 
43
2fmCon
 
31

Length

Max length6
Median length4
Mean length4.2993151
Min length4

Characters and Unicode

Total characters6277
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1Fam
2nd row1Fam
3rd row1Fam
4th row1Fam
5th row1Fam

Common Values

ValueCountFrequency (%)
1Fam 1220
83.6%
TwnhsE 114
 
7.8%
Duplex 52
 
3.6%
Twnhs 43
 
2.9%
2fmCon 31
 
2.1%

Length

2025-05-10T00:22:06.537076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-10T00:22:06.804947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1fam 1220
83.6%
twnhse 114
 
7.8%
duplex 52
 
3.6%
twnhs 43
 
2.9%
2fmcon 31
 
2.1%

Most occurring characters

ValueCountFrequency (%)
m 1251
19.9%
1 1220
19.4%
a 1220
19.4%
F 1220
19.4%
n 188
 
3.0%
T 157
 
2.5%
w 157
 
2.5%
h 157
 
2.5%
s 157
 
2.5%
E 114
 
1.8%
Other values (10) 436
 
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6277
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
m 1251
19.9%
1 1220
19.4%
a 1220
19.4%
F 1220
19.4%
n 188
 
3.0%
T 157
 
2.5%
w 157
 
2.5%
h 157
 
2.5%
s 157
 
2.5%
E 114
 
1.8%
Other values (10) 436
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6277
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
m 1251
19.9%
1 1220
19.4%
a 1220
19.4%
F 1220
19.4%
n 188
 
3.0%
T 157
 
2.5%
w 157
 
2.5%
h 157
 
2.5%
s 157
 
2.5%
E 114
 
1.8%
Other values (10) 436
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6277
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
m 1251
19.9%
1 1220
19.4%
a 1220
19.4%
F 1220
19.4%
n 188
 
3.0%
T 157
 
2.5%
w 157
 
2.5%
h 157
 
2.5%
s 157
 
2.5%
E 114
 
1.8%
Other values (10) 436
 
6.9%

HouseStyle
Categorical

High correlation 

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1Story
726 
2Story
445 
1.5Fin
154 
SLvl
 
65
SFoyer
 
37
Other values (3)
 
33

Length

Max length6
Median length6
Mean length5.9109589
Min length4

Characters and Unicode

Total characters8630
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2Story
2nd row1Story
3rd row2Story
4th row2Story
5th row2Story

Common Values

ValueCountFrequency (%)
1Story 726
49.7%
2Story 445
30.5%
1.5Fin 154
 
10.5%
SLvl 65
 
4.5%
SFoyer 37
 
2.5%
1.5Unf 14
 
1.0%
2.5Unf 11
 
0.8%
2.5Fin 8
 
0.5%

Length

2025-05-10T00:22:07.067186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-10T00:22:07.346659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1story 726
49.7%
2story 445
30.5%
1.5fin 154
 
10.5%
slvl 65
 
4.5%
sfoyer 37
 
2.5%
1.5unf 14
 
1.0%
2.5unf 11
 
0.8%
2.5fin 8
 
0.5%

Most occurring characters

ValueCountFrequency (%)
S 1273
14.8%
o 1208
14.0%
r 1208
14.0%
y 1208
14.0%
t 1171
13.6%
1 894
10.4%
2 464
 
5.4%
F 199
 
2.3%
5 187
 
2.2%
. 187
 
2.2%
Other values (8) 631
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8630
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 1273
14.8%
o 1208
14.0%
r 1208
14.0%
y 1208
14.0%
t 1171
13.6%
1 894
10.4%
2 464
 
5.4%
F 199
 
2.3%
5 187
 
2.2%
. 187
 
2.2%
Other values (8) 631
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8630
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 1273
14.8%
o 1208
14.0%
r 1208
14.0%
y 1208
14.0%
t 1171
13.6%
1 894
10.4%
2 464
 
5.4%
F 199
 
2.3%
5 187
 
2.2%
. 187
 
2.2%
Other values (8) 631
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8630
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 1273
14.8%
o 1208
14.0%
r 1208
14.0%
y 1208
14.0%
t 1171
13.6%
1 894
10.4%
2 464
 
5.4%
F 199
 
2.3%
5 187
 
2.2%
. 187
 
2.2%
Other values (8) 631
7.3%

OverallQual
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0993151
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-05-10T00:22:07.592669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median6
Q37
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3829965
Coefficient of variation (CV)0.22674621
Kurtosis0.096292778
Mean6.0993151
Median Absolute Deviation (MAD)1
Skewness0.21694393
Sum8905
Variance1.9126794
MonotonicityNot monotonic
2025-05-10T00:22:07.811904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5 397
27.2%
6 374
25.6%
7 319
21.8%
8 168
11.5%
4 116
 
7.9%
9 43
 
2.9%
3 20
 
1.4%
10 18
 
1.2%
2 3
 
0.2%
1 2
 
0.1%
ValueCountFrequency (%)
1 2
 
0.1%
2 3
 
0.2%
3 20
 
1.4%
4 116
 
7.9%
5 397
27.2%
6 374
25.6%
7 319
21.8%
8 168
11.5%
9 43
 
2.9%
10 18
 
1.2%
ValueCountFrequency (%)
10 18
 
1.2%
9 43
 
2.9%
8 168
11.5%
7 319
21.8%
6 374
25.6%
5 397
27.2%
4 116
 
7.9%
3 20
 
1.4%
2 3
 
0.2%
1 2
 
0.1%

OverallCond
Real number (ℝ)

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5753425
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-05-10T00:22:08.016677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median5
Q36
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1127993
Coefficient of variation (CV)0.199593
Kurtosis1.1064135
Mean5.5753425
Median Absolute Deviation (MAD)0
Skewness0.69306747
Sum8140
Variance1.2383224
MonotonicityNot monotonic
2025-05-10T00:22:08.250734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5 821
56.2%
6 252
 
17.3%
7 205
 
14.0%
8 72
 
4.9%
4 57
 
3.9%
3 25
 
1.7%
9 22
 
1.5%
2 5
 
0.3%
1 1
 
0.1%
ValueCountFrequency (%)
1 1
 
0.1%
2 5
 
0.3%
3 25
 
1.7%
4 57
 
3.9%
5 821
56.2%
6 252
 
17.3%
7 205
 
14.0%
8 72
 
4.9%
9 22
 
1.5%
ValueCountFrequency (%)
9 22
 
1.5%
8 72
 
4.9%
7 205
 
14.0%
6 252
 
17.3%
5 821
56.2%
4 57
 
3.9%
3 25
 
1.7%
2 5
 
0.3%
1 1
 
0.1%

YearBuilt
Real number (ℝ)

High correlation 

Distinct112
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.2678
Minimum1872
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-05-10T00:22:08.507618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1872
5-th percentile1916
Q11954
median1973
Q32000
95-th percentile2007
Maximum2010
Range138
Interquartile range (IQR)46

Descriptive statistics

Standard deviation30.202904
Coefficient of variation (CV)0.015321563
Kurtosis-0.43955194
Mean1971.2678
Median Absolute Deviation (MAD)25
Skewness-0.61346117
Sum2878051
Variance912.21541
MonotonicityNot monotonic
2025-05-10T00:22:08.814009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2006 67
 
4.6%
2005 64
 
4.4%
2004 54
 
3.7%
2007 49
 
3.4%
2003 45
 
3.1%
1976 33
 
2.3%
1977 32
 
2.2%
1920 30
 
2.1%
1959 26
 
1.8%
1998 25
 
1.7%
Other values (102) 1035
70.9%
ValueCountFrequency (%)
1872 1
 
0.1%
1875 1
 
0.1%
1880 4
 
0.3%
1882 1
 
0.1%
1885 2
 
0.1%
1890 2
 
0.1%
1892 2
 
0.1%
1893 1
 
0.1%
1898 1
 
0.1%
1900 10
0.7%
ValueCountFrequency (%)
2010 1
 
0.1%
2009 18
 
1.2%
2008 23
 
1.6%
2007 49
3.4%
2006 67
4.6%
2005 64
4.4%
2004 54
3.7%
2003 45
3.1%
2002 23
 
1.6%
2001 20
 
1.4%

YearRemodAdd
Real number (ℝ)

High correlation 

Distinct61
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1984.8658
Minimum1950
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-05-10T00:22:09.136702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1950
5-th percentile1950
Q11967
median1994
Q32004
95-th percentile2007
Maximum2010
Range60
Interquartile range (IQR)37

Descriptive statistics

Standard deviation20.645407
Coefficient of variation (CV)0.010401412
Kurtosis-1.2722452
Mean1984.8658
Median Absolute Deviation (MAD)13
Skewness-0.503562
Sum2897904
Variance426.23282
MonotonicityNot monotonic
2025-05-10T00:22:09.431073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1950 178
 
12.2%
2006 97
 
6.6%
2007 76
 
5.2%
2005 73
 
5.0%
2004 62
 
4.2%
2000 55
 
3.8%
2003 51
 
3.5%
2002 48
 
3.3%
2008 40
 
2.7%
1996 36
 
2.5%
Other values (51) 744
51.0%
ValueCountFrequency (%)
1950 178
12.2%
1951 4
 
0.3%
1952 5
 
0.3%
1953 10
 
0.7%
1954 14
 
1.0%
1955 9
 
0.6%
1956 10
 
0.7%
1957 9
 
0.6%
1958 15
 
1.0%
1959 18
 
1.2%
ValueCountFrequency (%)
2010 6
 
0.4%
2009 23
 
1.6%
2008 40
2.7%
2007 76
5.2%
2006 97
6.6%
2005 73
5.0%
2004 62
4.2%
2003 51
3.5%
2002 48
3.3%
2001 21
 
1.4%

RoofStyle
Categorical

Imbalance 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Gable
1141 
Hip
286 
Flat
 
13
Gambrel
 
11
Mansard
 
7

Length

Max length7
Median length5
Mean length4.6226027
Min length3

Characters and Unicode

Total characters6749
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGable
2nd rowGable
3rd rowGable
4th rowGable
5th rowGable

Common Values

ValueCountFrequency (%)
Gable 1141
78.2%
Hip 286
 
19.6%
Flat 13
 
0.9%
Gambrel 11
 
0.8%
Mansard 7
 
0.5%
Shed 2
 
0.1%

Length

2025-05-10T00:22:09.741672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-10T00:22:09.993976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
gable 1141
78.2%
hip 286
 
19.6%
flat 13
 
0.9%
gambrel 11
 
0.8%
mansard 7
 
0.5%
shed 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a 1179
17.5%
l 1165
17.3%
e 1154
17.1%
G 1152
17.1%
b 1152
17.1%
H 286
 
4.2%
i 286
 
4.2%
p 286
 
4.2%
r 18
 
0.3%
t 13
 
0.2%
Other values (8) 58
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6749
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1179
17.5%
l 1165
17.3%
e 1154
17.1%
G 1152
17.1%
b 1152
17.1%
H 286
 
4.2%
i 286
 
4.2%
p 286
 
4.2%
r 18
 
0.3%
t 13
 
0.2%
Other values (8) 58
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6749
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1179
17.5%
l 1165
17.3%
e 1154
17.1%
G 1152
17.1%
b 1152
17.1%
H 286
 
4.2%
i 286
 
4.2%
p 286
 
4.2%
r 18
 
0.3%
t 13
 
0.2%
Other values (8) 58
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6749
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1179
17.5%
l 1165
17.3%
e 1154
17.1%
G 1152
17.1%
b 1152
17.1%
H 286
 
4.2%
i 286
 
4.2%
p 286
 
4.2%
r 18
 
0.3%
t 13
 
0.2%
Other values (8) 58
 
0.9%

Exterior1st
Categorical

High correlation 

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
VinylSd
515 
HdBoard
222 
MetalSd
220 
Wd Sdng
206 
Plywood
108 
Other values (10)
189 

Length

Max length7
Median length7
Mean length6.9794521
Min length5

Characters and Unicode

Total characters10190
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st rowVinylSd
2nd rowMetalSd
3rd rowVinylSd
4th rowWd Sdng
5th rowVinylSd

Common Values

ValueCountFrequency (%)
VinylSd 515
35.3%
HdBoard 222
15.2%
MetalSd 220
15.1%
Wd Sdng 206
 
14.1%
Plywood 108
 
7.4%
CemntBd 61
 
4.2%
BrkFace 50
 
3.4%
WdShing 26
 
1.8%
Stucco 25
 
1.7%
AsbShng 20
 
1.4%
Other values (5) 7
 
0.5%

Length

2025-05-10T00:22:10.286577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
vinylsd 515
30.9%
hdboard 222
13.3%
metalsd 220
13.2%
wd 206
 
12.4%
sdng 206
 
12.4%
plywood 108
 
6.5%
cemntbd 61
 
3.7%
brkface 50
 
3.0%
wdshing 26
 
1.6%
stucco 25
 
1.5%
Other values (6) 27
 
1.6%

Most occurring characters

ValueCountFrequency (%)
d 1786
17.5%
S 1016
 
10.0%
l 844
 
8.3%
n 831
 
8.2%
y 623
 
6.1%
i 541
 
5.3%
V 515
 
5.1%
a 492
 
4.8%
o 468
 
4.6%
B 336
 
3.3%
Other values (22) 2738
26.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10190
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 1786
17.5%
S 1016
 
10.0%
l 844
 
8.3%
n 831
 
8.2%
y 623
 
6.1%
i 541
 
5.3%
V 515
 
5.1%
a 492
 
4.8%
o 468
 
4.6%
B 336
 
3.3%
Other values (22) 2738
26.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10190
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 1786
17.5%
S 1016
 
10.0%
l 844
 
8.3%
n 831
 
8.2%
y 623
 
6.1%
i 541
 
5.3%
V 515
 
5.1%
a 492
 
4.8%
o 468
 
4.6%
B 336
 
3.3%
Other values (22) 2738
26.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10190
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 1786
17.5%
S 1016
 
10.0%
l 844
 
8.3%
n 831
 
8.2%
y 623
 
6.1%
i 541
 
5.3%
V 515
 
5.1%
a 492
 
4.8%
o 468
 
4.6%
B 336
 
3.3%
Other values (22) 2738
26.9%

Exterior2nd
Categorical

High correlation 

Distinct16
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
VinylSd
504 
MetalSd
214 
HdBoard
207 
Wd Sdng
197 
Plywood
142 
Other values (11)
196 

Length

Max length7
Median length7
Mean length6.9732877
Min length5

Characters and Unicode

Total characters10181
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowVinylSd
2nd rowMetalSd
3rd rowVinylSd
4th rowWd Shng
5th rowVinylSd

Common Values

ValueCountFrequency (%)
VinylSd 504
34.5%
MetalSd 214
14.7%
HdBoard 207
14.2%
Wd Sdng 197
 
13.5%
Plywood 142
 
9.7%
CmentBd 60
 
4.1%
Wd Shng 38
 
2.6%
Stucco 26
 
1.8%
BrkFace 25
 
1.7%
AsbShng 20
 
1.4%
Other values (6) 27
 
1.8%

Length

2025-05-10T00:22:10.577955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
vinylsd 504
29.6%
wd 235
13.8%
metalsd 214
12.6%
hdboard 207
12.2%
sdng 197
 
11.6%
plywood 142
 
8.3%
cmentbd 60
 
3.5%
shng 38
 
2.2%
stucco 26
 
1.5%
brkface 25
 
1.5%
Other values (8) 54
 
3.2%

Most occurring characters

ValueCountFrequency (%)
d 1766
17.3%
S 1017
 
10.0%
l 861
 
8.5%
n 834
 
8.2%
y 646
 
6.3%
o 523
 
5.1%
V 504
 
5.0%
i 504
 
5.0%
a 446
 
4.4%
t 316
 
3.1%
Other values (23) 2764
27.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10181
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 1766
17.3%
S 1017
 
10.0%
l 861
 
8.5%
n 834
 
8.2%
y 646
 
6.3%
o 523
 
5.1%
V 504
 
5.0%
i 504
 
5.0%
a 446
 
4.4%
t 316
 
3.1%
Other values (23) 2764
27.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10181
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 1766
17.3%
S 1017
 
10.0%
l 861
 
8.5%
n 834
 
8.2%
y 646
 
6.3%
o 523
 
5.1%
V 504
 
5.0%
i 504
 
5.0%
a 446
 
4.4%
t 316
 
3.1%
Other values (23) 2764
27.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10181
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 1766
17.3%
S 1017
 
10.0%
l 861
 
8.5%
n 834
 
8.2%
y 646
 
6.3%
o 523
 
5.1%
V 504
 
5.0%
i 504
 
5.0%
a 446
 
4.4%
t 316
 
3.1%
Other values (23) 2764
27.1%

MasVnrType
Categorical

Missing 

Distinct3
Distinct (%)0.5%
Missing872
Missing (%)59.7%
Memory size11.5 KiB
BrkFace
445 
Stone
128 
BrkCmn
 
15

Length

Max length7
Median length7
Mean length6.5391156
Min length5

Characters and Unicode

Total characters3845
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBrkFace
2nd rowBrkFace
3rd rowBrkFace
4th rowStone
5th rowStone

Common Values

ValueCountFrequency (%)
BrkFace 445
30.5%
Stone 128
 
8.8%
BrkCmn 15
 
1.0%
(Missing) 872
59.7%

Length

2025-05-10T00:22:10.866720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-10T00:22:11.291027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
brkface 445
75.7%
stone 128
 
21.8%
brkcmn 15
 
2.6%

Most occurring characters

ValueCountFrequency (%)
e 573
14.9%
B 460
12.0%
r 460
12.0%
k 460
12.0%
F 445
11.6%
a 445
11.6%
c 445
11.6%
n 143
 
3.7%
S 128
 
3.3%
t 128
 
3.3%
Other values (3) 158
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3845
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 573
14.9%
B 460
12.0%
r 460
12.0%
k 460
12.0%
F 445
11.6%
a 445
11.6%
c 445
11.6%
n 143
 
3.7%
S 128
 
3.3%
t 128
 
3.3%
Other values (3) 158
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3845
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 573
14.9%
B 460
12.0%
r 460
12.0%
k 460
12.0%
F 445
11.6%
a 445
11.6%
c 445
11.6%
n 143
 
3.7%
S 128
 
3.3%
t 128
 
3.3%
Other values (3) 158
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3845
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 573
14.9%
B 460
12.0%
r 460
12.0%
k 460
12.0%
F 445
11.6%
a 445
11.6%
c 445
11.6%
n 143
 
3.7%
S 128
 
3.3%
t 128
 
3.3%
Other values (3) 158
 
4.1%

ExterQual
Categorical

High correlation 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
TA
906 
Gd
488 
Ex
 
52
Fa
 
14

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2920
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowTA
3rd rowGd
4th rowTA
5th rowGd

Common Values

ValueCountFrequency (%)
TA 906
62.1%
Gd 488
33.4%
Ex 52
 
3.6%
Fa 14
 
1.0%

Length

2025-05-10T00:22:11.557046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-10T00:22:11.777134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ta 906
62.1%
gd 488
33.4%
ex 52
 
3.6%
fa 14
 
1.0%

Most occurring characters

ValueCountFrequency (%)
T 906
31.0%
A 906
31.0%
G 488
16.7%
d 488
16.7%
E 52
 
1.8%
x 52
 
1.8%
F 14
 
0.5%
a 14
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 906
31.0%
A 906
31.0%
G 488
16.7%
d 488
16.7%
E 52
 
1.8%
x 52
 
1.8%
F 14
 
0.5%
a 14
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 906
31.0%
A 906
31.0%
G 488
16.7%
d 488
16.7%
E 52
 
1.8%
x 52
 
1.8%
F 14
 
0.5%
a 14
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 906
31.0%
A 906
31.0%
G 488
16.7%
d 488
16.7%
E 52
 
1.8%
x 52
 
1.8%
F 14
 
0.5%
a 14
 
0.5%

ExterCond
Categorical

Imbalance 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
TA
1282 
Gd
146 
Fa
 
28
Ex
 
3
Po
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2920
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA

Common Values

ValueCountFrequency (%)
TA 1282
87.8%
Gd 146
 
10.0%
Fa 28
 
1.9%
Ex 3
 
0.2%
Po 1
 
0.1%

Length

2025-05-10T00:22:12.015803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-10T00:22:12.248955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ta 1282
87.8%
gd 146
 
10.0%
fa 28
 
1.9%
ex 3
 
0.2%
po 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T 1282
43.9%
A 1282
43.9%
G 146
 
5.0%
d 146
 
5.0%
F 28
 
1.0%
a 28
 
1.0%
E 3
 
0.1%
x 3
 
0.1%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 1282
43.9%
A 1282
43.9%
G 146
 
5.0%
d 146
 
5.0%
F 28
 
1.0%
a 28
 
1.0%
E 3
 
0.1%
x 3
 
0.1%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 1282
43.9%
A 1282
43.9%
G 146
 
5.0%
d 146
 
5.0%
F 28
 
1.0%
a 28
 
1.0%
E 3
 
0.1%
x 3
 
0.1%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 1282
43.9%
A 1282
43.9%
G 146
 
5.0%
d 146
 
5.0%
F 28
 
1.0%
a 28
 
1.0%
E 3
 
0.1%
x 3
 
0.1%
P 1
 
< 0.1%
o 1
 
< 0.1%

Foundation
Categorical

High correlation 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
PConc
647 
CBlock
634 
BrkTil
146 
Slab
 
24
Stone
 
6

Length

Max length6
Median length6
Mean length5.5157534
Min length4

Characters and Unicode

Total characters8053
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPConc
2nd rowCBlock
3rd rowPConc
4th rowBrkTil
5th rowPConc

Common Values

ValueCountFrequency (%)
PConc 647
44.3%
CBlock 634
43.4%
BrkTil 146
 
10.0%
Slab 24
 
1.6%
Stone 6
 
0.4%
Wood 3
 
0.2%

Length

2025-05-10T00:22:12.526393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-10T00:22:12.781396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
pconc 647
44.3%
cblock 634
43.4%
brktil 146
 
10.0%
slab 24
 
1.6%
stone 6
 
0.4%
wood 3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
o 1293
16.1%
C 1281
15.9%
c 1281
15.9%
l 804
10.0%
B 780
9.7%
k 780
9.7%
n 653
8.1%
P 647
8.0%
i 146
 
1.8%
T 146
 
1.8%
Other values (8) 242
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8053
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1293
16.1%
C 1281
15.9%
c 1281
15.9%
l 804
10.0%
B 780
9.7%
k 780
9.7%
n 653
8.1%
P 647
8.0%
i 146
 
1.8%
T 146
 
1.8%
Other values (8) 242
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8053
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1293
16.1%
C 1281
15.9%
c 1281
15.9%
l 804
10.0%
B 780
9.7%
k 780
9.7%
n 653
8.1%
P 647
8.0%
i 146
 
1.8%
T 146
 
1.8%
Other values (8) 242
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8053
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1293
16.1%
C 1281
15.9%
c 1281
15.9%
l 804
10.0%
B 780
9.7%
k 780
9.7%
n 653
8.1%
P 647
8.0%
i 146
 
1.8%
T 146
 
1.8%
Other values (8) 242
 
3.0%

BsmtQual
Categorical

High correlation  Missing 

Distinct4
Distinct (%)0.3%
Missing37
Missing (%)2.5%
Memory size11.5 KiB
TA
649 
Gd
618 
Ex
121 
Fa
 
35

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2846
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowGd
3rd rowGd
4th rowTA
5th rowGd

Common Values

ValueCountFrequency (%)
TA 649
44.5%
Gd 618
42.3%
Ex 121
 
8.3%
Fa 35
 
2.4%
(Missing) 37
 
2.5%

Length

2025-05-10T00:22:13.051146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-10T00:22:13.276309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ta 649
45.6%
gd 618
43.4%
ex 121
 
8.5%
fa 35
 
2.5%

Most occurring characters

ValueCountFrequency (%)
T 649
22.8%
A 649
22.8%
G 618
21.7%
d 618
21.7%
E 121
 
4.3%
x 121
 
4.3%
F 35
 
1.2%
a 35
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2846
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 649
22.8%
A 649
22.8%
G 618
21.7%
d 618
21.7%
E 121
 
4.3%
x 121
 
4.3%
F 35
 
1.2%
a 35
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2846
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 649
22.8%
A 649
22.8%
G 618
21.7%
d 618
21.7%
E 121
 
4.3%
x 121
 
4.3%
F 35
 
1.2%
a 35
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2846
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 649
22.8%
A 649
22.8%
G 618
21.7%
d 618
21.7%
E 121
 
4.3%
x 121
 
4.3%
F 35
 
1.2%
a 35
 
1.2%

BsmtExposure
Categorical

Missing 

Distinct4
Distinct (%)0.3%
Missing38
Missing (%)2.6%
Memory size11.5 KiB
No
953 
Av
221 
Gd
134 
Mn
114 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2844
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowGd
3rd rowMn
4th rowNo
5th rowAv

Common Values

ValueCountFrequency (%)
No 953
65.3%
Av 221
 
15.1%
Gd 134
 
9.2%
Mn 114
 
7.8%
(Missing) 38
 
2.6%

Length

2025-05-10T00:22:13.526685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-10T00:22:13.771561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
no 953
67.0%
av 221
 
15.5%
gd 134
 
9.4%
mn 114
 
8.0%

Most occurring characters

ValueCountFrequency (%)
N 953
33.5%
o 953
33.5%
A 221
 
7.8%
v 221
 
7.8%
G 134
 
4.7%
d 134
 
4.7%
M 114
 
4.0%
n 114
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2844
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 953
33.5%
o 953
33.5%
A 221
 
7.8%
v 221
 
7.8%
G 134
 
4.7%
d 134
 
4.7%
M 114
 
4.0%
n 114
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2844
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 953
33.5%
o 953
33.5%
A 221
 
7.8%
v 221
 
7.8%
G 134
 
4.7%
d 134
 
4.7%
M 114
 
4.0%
n 114
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2844
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 953
33.5%
o 953
33.5%
A 221
 
7.8%
v 221
 
7.8%
G 134
 
4.7%
d 134
 
4.7%
M 114
 
4.0%
n 114
 
4.0%

BsmtFinType1
Categorical

Missing 

Distinct6
Distinct (%)0.4%
Missing37
Missing (%)2.5%
Memory size11.5 KiB
Unf
430 
GLQ
418 
ALQ
220 
BLQ
148 
Rec
133 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4269
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGLQ
2nd rowALQ
3rd rowGLQ
4th rowALQ
5th rowGLQ

Common Values

ValueCountFrequency (%)
Unf 430
29.5%
GLQ 418
28.6%
ALQ 220
15.1%
BLQ 148
 
10.1%
Rec 133
 
9.1%
LwQ 74
 
5.1%
(Missing) 37
 
2.5%

Length

2025-05-10T00:22:14.044020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-10T00:22:14.301220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
unf 430
30.2%
glq 418
29.4%
alq 220
15.5%
blq 148
 
10.4%
rec 133
 
9.3%
lwq 74
 
5.2%

Most occurring characters

ValueCountFrequency (%)
L 860
20.1%
Q 860
20.1%
U 430
10.1%
n 430
10.1%
f 430
10.1%
G 418
9.8%
A 220
 
5.2%
B 148
 
3.5%
R 133
 
3.1%
e 133
 
3.1%
Other values (2) 207
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4269
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 860
20.1%
Q 860
20.1%
U 430
10.1%
n 430
10.1%
f 430
10.1%
G 418
9.8%
A 220
 
5.2%
B 148
 
3.5%
R 133
 
3.1%
e 133
 
3.1%
Other values (2) 207
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4269
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 860
20.1%
Q 860
20.1%
U 430
10.1%
n 430
10.1%
f 430
10.1%
G 418
9.8%
A 220
 
5.2%
B 148
 
3.5%
R 133
 
3.1%
e 133
 
3.1%
Other values (2) 207
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4269
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 860
20.1%
Q 860
20.1%
U 430
10.1%
n 430
10.1%
f 430
10.1%
G 418
9.8%
A 220
 
5.2%
B 148
 
3.5%
R 133
 
3.1%
e 133
 
3.1%
Other values (2) 207
 
4.8%

BsmtFinSF1
Real number (ℝ)

High correlation  Zeros 

Distinct637
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean443.63973
Minimum0
Maximum5644
Zeros467
Zeros (%)32.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-05-10T00:22:14.585988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median383.5
Q3712.25
95-th percentile1274
Maximum5644
Range5644
Interquartile range (IQR)712.25

Descriptive statistics

Standard deviation456.09809
Coefficient of variation (CV)1.0280822
Kurtosis11.118236
Mean443.63973
Median Absolute Deviation (MAD)383.5
Skewness1.6855031
Sum647714
Variance208025.47
MonotonicityNot monotonic
2025-05-10T00:22:14.879953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 467
32.0%
24 12
 
0.8%
16 9
 
0.6%
686 5
 
0.3%
662 5
 
0.3%
20 5
 
0.3%
936 5
 
0.3%
616 5
 
0.3%
560 4
 
0.3%
553 4
 
0.3%
Other values (627) 939
64.3%
ValueCountFrequency (%)
0 467
32.0%
2 1
 
0.1%
16 9
 
0.6%
20 5
 
0.3%
24 12
 
0.8%
25 1
 
0.1%
27 1
 
0.1%
28 3
 
0.2%
33 1
 
0.1%
35 1
 
0.1%
ValueCountFrequency (%)
5644 1
0.1%
2260 1
0.1%
2188 1
0.1%
2096 1
0.1%
1904 1
0.1%
1880 1
0.1%
1810 1
0.1%
1767 1
0.1%
1721 1
0.1%
1696 1
0.1%

BsmtUnfSF
Real number (ℝ)

High correlation  Zeros 

Distinct780
Distinct (%)53.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean567.24041
Minimum0
Maximum2336
Zeros118
Zeros (%)8.1%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-05-10T00:22:15.165702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1223
median477.5
Q3808
95-th percentile1468
Maximum2336
Range2336
Interquartile range (IQR)585

Descriptive statistics

Standard deviation441.86696
Coefficient of variation (CV)0.77897651
Kurtosis0.47499399
Mean567.24041
Median Absolute Deviation (MAD)288
Skewness0.92026845
Sum828171
Variance195246.41
MonotonicityNot monotonic
2025-05-10T00:22:15.460974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 118
 
8.1%
728 9
 
0.6%
384 8
 
0.5%
600 7
 
0.5%
300 7
 
0.5%
572 7
 
0.5%
270 6
 
0.4%
625 6
 
0.4%
672 6
 
0.4%
440 6
 
0.4%
Other values (770) 1280
87.7%
ValueCountFrequency (%)
0 118
8.1%
14 1
 
0.1%
15 1
 
0.1%
23 2
 
0.1%
26 1
 
0.1%
29 1
 
0.1%
30 1
 
0.1%
32 2
 
0.1%
35 1
 
0.1%
36 4
 
0.3%
ValueCountFrequency (%)
2336 1
0.1%
2153 1
0.1%
2121 1
0.1%
2046 1
0.1%
2042 1
0.1%
2002 1
0.1%
1969 1
0.1%
1935 1
0.1%
1926 1
0.1%
1907 1
0.1%

TotalBsmtSF
Real number (ℝ)

High correlation  Zeros 

Distinct721
Distinct (%)49.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1057.4295
Minimum0
Maximum6110
Zeros37
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-05-10T00:22:15.751053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile519.3
Q1795.75
median991.5
Q31298.25
95-th percentile1753
Maximum6110
Range6110
Interquartile range (IQR)502.5

Descriptive statistics

Standard deviation438.70532
Coefficient of variation (CV)0.41487905
Kurtosis13.250483
Mean1057.4295
Median Absolute Deviation (MAD)234.5
Skewness1.5242545
Sum1543847
Variance192462.36
MonotonicityNot monotonic
2025-05-10T00:22:16.056266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37
 
2.5%
864 35
 
2.4%
672 17
 
1.2%
912 15
 
1.0%
1040 14
 
1.0%
816 13
 
0.9%
768 12
 
0.8%
728 12
 
0.8%
894 11
 
0.8%
780 11
 
0.8%
Other values (711) 1283
87.9%
ValueCountFrequency (%)
0 37
2.5%
105 1
 
0.1%
190 1
 
0.1%
264 3
 
0.2%
270 1
 
0.1%
290 1
 
0.1%
319 1
 
0.1%
360 1
 
0.1%
372 1
 
0.1%
384 7
 
0.5%
ValueCountFrequency (%)
6110 1
0.1%
3206 1
0.1%
3200 1
0.1%
3138 1
0.1%
3094 1
0.1%
2633 1
0.1%
2524 1
0.1%
2444 1
0.1%
2396 1
0.1%
2392 1
0.1%

HeatingQC
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Ex
741 
TA
428 
Gd
241 
Fa
 
49
Po
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2920
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowEx
2nd rowEx
3rd rowEx
4th rowGd
5th rowEx

Common Values

ValueCountFrequency (%)
Ex 741
50.8%
TA 428
29.3%
Gd 241
 
16.5%
Fa 49
 
3.4%
Po 1
 
0.1%

Length

2025-05-10T00:22:16.342995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-10T00:22:16.577350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ex 741
50.8%
ta 428
29.3%
gd 241
 
16.5%
fa 49
 
3.4%
po 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
E 741
25.4%
x 741
25.4%
T 428
14.7%
A 428
14.7%
G 241
 
8.3%
d 241
 
8.3%
F 49
 
1.7%
a 49
 
1.7%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 741
25.4%
x 741
25.4%
T 428
14.7%
A 428
14.7%
G 241
 
8.3%
d 241
 
8.3%
F 49
 
1.7%
a 49
 
1.7%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 741
25.4%
x 741
25.4%
T 428
14.7%
A 428
14.7%
G 241
 
8.3%
d 241
 
8.3%
F 49
 
1.7%
a 49
 
1.7%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 741
25.4%
x 741
25.4%
T 428
14.7%
A 428
14.7%
G 241
 
8.3%
d 241
 
8.3%
F 49
 
1.7%
a 49
 
1.7%
P 1
 
< 0.1%
o 1
 
< 0.1%

CentralAir
Boolean

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
True
1365 
False
 
95
ValueCountFrequency (%)
True 1365
93.5%
False 95
 
6.5%
2025-05-10T00:22:16.787454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

1stFlrSF
Real number (ℝ)

High correlation 

Distinct753
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1162.6267
Minimum334
Maximum4692
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-05-10T00:22:17.027846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile672.95
Q1882
median1087
Q31391.25
95-th percentile1831.25
Maximum4692
Range4358
Interquartile range (IQR)509.25

Descriptive statistics

Standard deviation386.58774
Coefficient of variation (CV)0.33251235
Kurtosis5.7458415
Mean1162.6267
Median Absolute Deviation (MAD)234.5
Skewness1.3767566
Sum1697435
Variance149450.08
MonotonicityNot monotonic
2025-05-10T00:22:17.311222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
864 25
 
1.7%
1040 16
 
1.1%
912 14
 
1.0%
894 12
 
0.8%
848 12
 
0.8%
672 11
 
0.8%
630 9
 
0.6%
816 9
 
0.6%
483 7
 
0.5%
960 7
 
0.5%
Other values (743) 1338
91.6%
ValueCountFrequency (%)
334 1
 
0.1%
372 1
 
0.1%
438 1
 
0.1%
480 1
 
0.1%
483 7
0.5%
495 1
 
0.1%
520 5
0.3%
525 1
 
0.1%
526 1
 
0.1%
536 1
 
0.1%
ValueCountFrequency (%)
4692 1
0.1%
3228 1
0.1%
3138 1
0.1%
2898 1
0.1%
2633 1
0.1%
2524 1
0.1%
2515 1
0.1%
2444 1
0.1%
2411 1
0.1%
2402 1
0.1%

GrLivArea
Real number (ℝ)

High correlation 

Distinct861
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1515.4637
Minimum334
Maximum5642
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-05-10T00:22:17.589363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile848
Q11129.5
median1464
Q31776.75
95-th percentile2466.1
Maximum5642
Range5308
Interquartile range (IQR)647.25

Descriptive statistics

Standard deviation525.48038
Coefficient of variation (CV)0.34674561
Kurtosis4.8951206
Mean1515.4637
Median Absolute Deviation (MAD)326
Skewness1.3665604
Sum2212577
Variance276129.63
MonotonicityNot monotonic
2025-05-10T00:22:18.059488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
864 22
 
1.5%
1040 14
 
1.0%
894 11
 
0.8%
1456 10
 
0.7%
848 10
 
0.7%
1200 9
 
0.6%
912 9
 
0.6%
816 8
 
0.5%
1092 8
 
0.5%
1728 7
 
0.5%
Other values (851) 1352
92.6%
ValueCountFrequency (%)
334 1
 
0.1%
438 1
 
0.1%
480 1
 
0.1%
520 1
 
0.1%
605 1
 
0.1%
616 1
 
0.1%
630 6
0.4%
672 2
 
0.1%
691 1
 
0.1%
693 1
 
0.1%
ValueCountFrequency (%)
5642 1
0.1%
4676 1
0.1%
4476 1
0.1%
4316 1
0.1%
3627 1
0.1%
3608 1
0.1%
3493 1
0.1%
3447 1
0.1%
3395 1
0.1%
3279 1
0.1%

BsmtFullBath
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
856 
1
588 
2
 
15
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 856
58.6%
1 588
40.3%
2 15
 
1.0%
3 1
 
0.1%

Length

2025-05-10T00:22:18.324765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-10T00:22:18.553960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 856
58.6%
1 588
40.3%
2 15
 
1.0%
3 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 856
58.6%
1 588
40.3%
2 15
 
1.0%
3 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 856
58.6%
1 588
40.3%
2 15
 
1.0%
3 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 856
58.6%
1 588
40.3%
2 15
 
1.0%
3 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 856
58.6%
1 588
40.3%
2 15
 
1.0%
3 1
 
0.1%

FullBath
Categorical

High correlation 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2
768 
1
650 
3
 
33
0
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 768
52.6%
1 650
44.5%
3 33
 
2.3%
0 9
 
0.6%

Length

2025-05-10T00:22:18.800916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-10T00:22:19.013704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 768
52.6%
1 650
44.5%
3 33
 
2.3%
0 9
 
0.6%

Most occurring characters

ValueCountFrequency (%)
2 768
52.6%
1 650
44.5%
3 33
 
2.3%
0 9
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 768
52.6%
1 650
44.5%
3 33
 
2.3%
0 9
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 768
52.6%
1 650
44.5%
3 33
 
2.3%
0 9
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 768
52.6%
1 650
44.5%
3 33
 
2.3%
0 9
 
0.6%

HalfBath
Categorical

High correlation 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
913 
1
535 
2
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 913
62.5%
1 535
36.6%
2 12
 
0.8%

Length

2025-05-10T00:22:19.264642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-10T00:22:19.487475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 913
62.5%
1 535
36.6%
2 12
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 913
62.5%
1 535
36.6%
2 12
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 913
62.5%
1 535
36.6%
2 12
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 913
62.5%
1 535
36.6%
2 12
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 913
62.5%
1 535
36.6%
2 12
 
0.8%

BedroomAbvGr
Real number (ℝ)

High correlation 

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8664384
Minimum0
Maximum8
Zeros6
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-05-10T00:22:19.693555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q33
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.81577804
Coefficient of variation (CV)0.2845964
Kurtosis2.2308746
Mean2.8664384
Median Absolute Deviation (MAD)0
Skewness0.2117901
Sum4185
Variance0.66549382
MonotonicityNot monotonic
2025-05-10T00:22:19.921082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 804
55.1%
2 358
24.5%
4 213
 
14.6%
1 50
 
3.4%
5 21
 
1.4%
6 7
 
0.5%
0 6
 
0.4%
8 1
 
0.1%
ValueCountFrequency (%)
0 6
 
0.4%
1 50
 
3.4%
2 358
24.5%
3 804
55.1%
4 213
 
14.6%
5 21
 
1.4%
6 7
 
0.5%
8 1
 
0.1%
ValueCountFrequency (%)
8 1
 
0.1%
6 7
 
0.5%
5 21
 
1.4%
4 213
 
14.6%
3 804
55.1%
2 358
24.5%
1 50
 
3.4%
0 6
 
0.4%

KitchenAbvGr
Categorical

Imbalance 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1
1392 
2
 
65
3
 
2
0
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1392
95.3%
2 65
 
4.5%
3 2
 
0.1%
0 1
 
0.1%

Length

2025-05-10T00:22:20.188689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-10T00:22:20.409180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1392
95.3%
2 65
 
4.5%
3 2
 
0.1%
0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 1392
95.3%
2 65
 
4.5%
3 2
 
0.1%
0 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1392
95.3%
2 65
 
4.5%
3 2
 
0.1%
0 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1392
95.3%
2 65
 
4.5%
3 2
 
0.1%
0 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1392
95.3%
2 65
 
4.5%
3 2
 
0.1%
0 1
 
0.1%

KitchenQual
Categorical

High correlation 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
TA
735 
Gd
586 
Ex
100 
Fa
 
39

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2920
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowTA
3rd rowGd
4th rowGd
5th rowGd

Common Values

ValueCountFrequency (%)
TA 735
50.3%
Gd 586
40.1%
Ex 100
 
6.8%
Fa 39
 
2.7%

Length

2025-05-10T00:22:20.642859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-10T00:22:20.884885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ta 735
50.3%
gd 586
40.1%
ex 100
 
6.8%
fa 39
 
2.7%

Most occurring characters

ValueCountFrequency (%)
T 735
25.2%
A 735
25.2%
G 586
20.1%
d 586
20.1%
E 100
 
3.4%
x 100
 
3.4%
F 39
 
1.3%
a 39
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 735
25.2%
A 735
25.2%
G 586
20.1%
d 586
20.1%
E 100
 
3.4%
x 100
 
3.4%
F 39
 
1.3%
a 39
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 735
25.2%
A 735
25.2%
G 586
20.1%
d 586
20.1%
E 100
 
3.4%
x 100
 
3.4%
F 39
 
1.3%
a 39
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 735
25.2%
A 735
25.2%
G 586
20.1%
d 586
20.1%
E 100
 
3.4%
x 100
 
3.4%
F 39
 
1.3%
a 39
 
1.3%

TotRmsAbvGrd
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5178082
Minimum2
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-05-10T00:22:21.105548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q15
median6
Q37
95-th percentile10
Maximum14
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6253933
Coefficient of variation (CV)0.24937728
Kurtosis0.88076157
Mean6.5178082
Median Absolute Deviation (MAD)1
Skewness0.67634084
Sum9516
Variance2.6419033
MonotonicityNot monotonic
2025-05-10T00:22:21.339357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 402
27.5%
7 329
22.5%
5 275
18.8%
8 187
12.8%
4 97
 
6.6%
9 75
 
5.1%
10 47
 
3.2%
11 18
 
1.2%
3 17
 
1.2%
12 11
 
0.8%
Other values (2) 2
 
0.1%
ValueCountFrequency (%)
2 1
 
0.1%
3 17
 
1.2%
4 97
 
6.6%
5 275
18.8%
6 402
27.5%
7 329
22.5%
8 187
12.8%
9 75
 
5.1%
10 47
 
3.2%
11 18
 
1.2%
ValueCountFrequency (%)
14 1
 
0.1%
12 11
 
0.8%
11 18
 
1.2%
10 47
 
3.2%
9 75
 
5.1%
8 187
12.8%
7 329
22.5%
6 402
27.5%
5 275
18.8%
4 97
 
6.6%

Fireplaces
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
690 
1
650 
2
115 
3
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 690
47.3%
1 650
44.5%
2 115
 
7.9%
3 5
 
0.3%

Length

2025-05-10T00:22:21.587585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-10T00:22:21.811510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 690
47.3%
1 650
44.5%
2 115
 
7.9%
3 5
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 690
47.3%
1 650
44.5%
2 115
 
7.9%
3 5
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 690
47.3%
1 650
44.5%
2 115
 
7.9%
3 5
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 690
47.3%
1 650
44.5%
2 115
 
7.9%
3 5
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 690
47.3%
1 650
44.5%
2 115
 
7.9%
3 5
 
0.3%

FireplaceQu
Categorical

Missing 

Distinct5
Distinct (%)0.6%
Missing690
Missing (%)47.3%
Memory size11.5 KiB
Gd
380 
TA
313 
Fa
 
33
Ex
 
24
Po
 
20

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1540
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowGd
4th rowTA
5th rowGd

Common Values

ValueCountFrequency (%)
Gd 380
26.0%
TA 313
21.4%
Fa 33
 
2.3%
Ex 24
 
1.6%
Po 20
 
1.4%
(Missing) 690
47.3%

Length

2025-05-10T00:22:22.037678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-10T00:22:22.257920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
gd 380
49.4%
ta 313
40.6%
fa 33
 
4.3%
ex 24
 
3.1%
po 20
 
2.6%

Most occurring characters

ValueCountFrequency (%)
G 380
24.7%
d 380
24.7%
T 313
20.3%
A 313
20.3%
F 33
 
2.1%
a 33
 
2.1%
E 24
 
1.6%
x 24
 
1.6%
P 20
 
1.3%
o 20
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1540
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 380
24.7%
d 380
24.7%
T 313
20.3%
A 313
20.3%
F 33
 
2.1%
a 33
 
2.1%
E 24
 
1.6%
x 24
 
1.6%
P 20
 
1.3%
o 20
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1540
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 380
24.7%
d 380
24.7%
T 313
20.3%
A 313
20.3%
F 33
 
2.1%
a 33
 
2.1%
E 24
 
1.6%
x 24
 
1.6%
P 20
 
1.3%
o 20
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1540
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 380
24.7%
d 380
24.7%
T 313
20.3%
A 313
20.3%
F 33
 
2.1%
a 33
 
2.1%
E 24
 
1.6%
x 24
 
1.6%
P 20
 
1.3%
o 20
 
1.3%

GarageType
Categorical

Missing 

Distinct6
Distinct (%)0.4%
Missing81
Missing (%)5.5%
Memory size11.5 KiB
Attchd
870 
Detchd
387 
BuiltIn
88 
Basment
 
19
CarPort
 
9

Length

Max length7
Median length6
Mean length6.0841189
Min length6

Characters and Unicode

Total characters8390
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAttchd
2nd rowAttchd
3rd rowAttchd
4th rowDetchd
5th rowAttchd

Common Values

ValueCountFrequency (%)
Attchd 870
59.6%
Detchd 387
26.5%
BuiltIn 88
 
6.0%
Basment 19
 
1.3%
CarPort 9
 
0.6%
2Types 6
 
0.4%
(Missing) 81
 
5.5%

Length

2025-05-10T00:22:22.505621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-10T00:22:22.739249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
attchd 870
63.1%
detchd 387
28.1%
builtin 88
 
6.4%
basment 19
 
1.4%
carport 9
 
0.7%
2types 6
 
0.4%

Most occurring characters

ValueCountFrequency (%)
t 2243
26.7%
c 1257
15.0%
h 1257
15.0%
d 1257
15.0%
A 870
 
10.4%
e 412
 
4.9%
D 387
 
4.6%
n 107
 
1.3%
B 107
 
1.3%
u 88
 
1.0%
Other values (14) 405
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8390
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 2243
26.7%
c 1257
15.0%
h 1257
15.0%
d 1257
15.0%
A 870
 
10.4%
e 412
 
4.9%
D 387
 
4.6%
n 107
 
1.3%
B 107
 
1.3%
u 88
 
1.0%
Other values (14) 405
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8390
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 2243
26.7%
c 1257
15.0%
h 1257
15.0%
d 1257
15.0%
A 870
 
10.4%
e 412
 
4.9%
D 387
 
4.6%
n 107
 
1.3%
B 107
 
1.3%
u 88
 
1.0%
Other values (14) 405
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8390
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 2243
26.7%
c 1257
15.0%
h 1257
15.0%
d 1257
15.0%
A 870
 
10.4%
e 412
 
4.9%
D 387
 
4.6%
n 107
 
1.3%
B 107
 
1.3%
u 88
 
1.0%
Other values (14) 405
 
4.8%

GarageFinish
Categorical

High correlation  Missing 

Distinct3
Distinct (%)0.2%
Missing81
Missing (%)5.5%
Memory size11.5 KiB
Unf
605 
RFn
422 
Fin
352 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4137
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRFn
2nd rowRFn
3rd rowRFn
4th rowUnf
5th rowRFn

Common Values

ValueCountFrequency (%)
Unf 605
41.4%
RFn 422
28.9%
Fin 352
24.1%
(Missing) 81
 
5.5%

Length

2025-05-10T00:22:22.999656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-10T00:22:23.230316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
unf 605
43.9%
rfn 422
30.6%
fin 352
25.5%

Most occurring characters

ValueCountFrequency (%)
n 1379
33.3%
F 774
18.7%
U 605
14.6%
f 605
14.6%
R 422
 
10.2%
i 352
 
8.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4137
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 1379
33.3%
F 774
18.7%
U 605
14.6%
f 605
14.6%
R 422
 
10.2%
i 352
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4137
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 1379
33.3%
F 774
18.7%
U 605
14.6%
f 605
14.6%
R 422
 
10.2%
i 352
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4137
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 1379
33.3%
F 774
18.7%
U 605
14.6%
f 605
14.6%
R 422
 
10.2%
i 352
 
8.5%

GarageCars
Categorical

High correlation 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2
824 
1
369 
3
181 
0
 
81
4
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
2 824
56.4%
1 369
25.3%
3 181
 
12.4%
0 81
 
5.5%
4 5
 
0.3%

Length

2025-05-10T00:22:23.471724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-10T00:22:23.702248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 824
56.4%
1 369
25.3%
3 181
 
12.4%
0 81
 
5.5%
4 5
 
0.3%

Most occurring characters

ValueCountFrequency (%)
2 824
56.4%
1 369
25.3%
3 181
 
12.4%
0 81
 
5.5%
4 5
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 824
56.4%
1 369
25.3%
3 181
 
12.4%
0 81
 
5.5%
4 5
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 824
56.4%
1 369
25.3%
3 181
 
12.4%
0 81
 
5.5%
4 5
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 824
56.4%
1 369
25.3%
3 181
 
12.4%
0 81
 
5.5%
4 5
 
0.3%

GarageArea
Real number (ℝ)

High correlation  Zeros 

Distinct441
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean472.98014
Minimum0
Maximum1418
Zeros81
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-05-10T00:22:23.968365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1334.5
median480
Q3576
95-th percentile850.1
Maximum1418
Range1418
Interquartile range (IQR)241.5

Descriptive statistics

Standard deviation213.80484
Coefficient of variation (CV)0.45203768
Kurtosis0.9170672
Mean472.98014
Median Absolute Deviation (MAD)120
Skewness0.17998091
Sum690551
Variance45712.51
MonotonicityNot monotonic
2025-05-10T00:22:24.271186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 81
 
5.5%
440 49
 
3.4%
576 47
 
3.2%
240 38
 
2.6%
484 34
 
2.3%
528 33
 
2.3%
288 27
 
1.8%
400 25
 
1.7%
264 24
 
1.6%
480 24
 
1.6%
Other values (431) 1078
73.8%
ValueCountFrequency (%)
0 81
5.5%
160 2
 
0.1%
164 1
 
0.1%
180 9
 
0.6%
186 1
 
0.1%
189 1
 
0.1%
192 1
 
0.1%
198 1
 
0.1%
200 4
 
0.3%
205 3
 
0.2%
ValueCountFrequency (%)
1418 1
0.1%
1390 1
0.1%
1356 1
0.1%
1248 1
0.1%
1220 1
0.1%
1166 1
0.1%
1134 1
0.1%
1069 1
0.1%
1053 1
0.1%
1052 2
0.1%

Fence
Categorical

Missing 

Distinct4
Distinct (%)1.4%
Missing1179
Missing (%)80.8%
Memory size11.5 KiB
MnPrv
157 
GdPrv
59 
GdWo
54 
MnWw
 
11

Length

Max length5
Median length5
Mean length4.7686833
Min length4

Characters and Unicode

Total characters1340
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMnPrv
2nd rowGdWo
3rd rowGdPrv
4th rowMnPrv
5th rowGdPrv

Common Values

ValueCountFrequency (%)
MnPrv 157
 
10.8%
GdPrv 59
 
4.0%
GdWo 54
 
3.7%
MnWw 11
 
0.8%
(Missing) 1179
80.8%

Length

2025-05-10T00:22:24.552035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-10T00:22:24.773652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
mnprv 157
55.9%
gdprv 59
 
21.0%
gdwo 54
 
19.2%
mnww 11
 
3.9%

Most occurring characters

ValueCountFrequency (%)
P 216
16.1%
r 216
16.1%
v 216
16.1%
M 168
12.5%
n 168
12.5%
G 113
8.4%
d 113
8.4%
W 65
 
4.9%
o 54
 
4.0%
w 11
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1340
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 216
16.1%
r 216
16.1%
v 216
16.1%
M 168
12.5%
n 168
12.5%
G 113
8.4%
d 113
8.4%
W 65
 
4.9%
o 54
 
4.0%
w 11
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1340
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 216
16.1%
r 216
16.1%
v 216
16.1%
M 168
12.5%
n 168
12.5%
G 113
8.4%
d 113
8.4%
W 65
 
4.9%
o 54
 
4.0%
w 11
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1340
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 216
16.1%
r 216
16.1%
v 216
16.1%
M 168
12.5%
n 168
12.5%
G 113
8.4%
d 113
8.4%
W 65
 
4.9%
o 54
 
4.0%
w 11
 
0.8%

MiscFeature
Categorical

High correlation  Imbalance  Missing 

Distinct4
Distinct (%)7.4%
Missing1406
Missing (%)96.3%
Memory size11.5 KiB
Shed
49 
Gar2
 
2
Othr
 
2
TenC
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters216
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.9%

Sample

1st rowShed
2nd rowShed
3rd rowShed
4th rowShed
5th rowShed

Common Values

ValueCountFrequency (%)
Shed 49
 
3.4%
Gar2 2
 
0.1%
Othr 2
 
0.1%
TenC 1
 
0.1%
(Missing) 1406
96.3%

Length

2025-05-10T00:22:25.008361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-10T00:22:25.225571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
shed 49
90.7%
gar2 2
 
3.7%
othr 2
 
3.7%
tenc 1
 
1.9%

Most occurring characters

ValueCountFrequency (%)
h 51
23.6%
e 50
23.1%
S 49
22.7%
d 49
22.7%
r 4
 
1.9%
G 2
 
0.9%
a 2
 
0.9%
2 2
 
0.9%
O 2
 
0.9%
t 2
 
0.9%
Other values (3) 3
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 216
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
h 51
23.6%
e 50
23.1%
S 49
22.7%
d 49
22.7%
r 4
 
1.9%
G 2
 
0.9%
a 2
 
0.9%
2 2
 
0.9%
O 2
 
0.9%
t 2
 
0.9%
Other values (3) 3
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 216
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
h 51
23.6%
e 50
23.1%
S 49
22.7%
d 49
22.7%
r 4
 
1.9%
G 2
 
0.9%
a 2
 
0.9%
2 2
 
0.9%
O 2
 
0.9%
t 2
 
0.9%
Other values (3) 3
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 216
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
h 51
23.6%
e 50
23.1%
S 49
22.7%
d 49
22.7%
r 4
 
1.9%
G 2
 
0.9%
a 2
 
0.9%
2 2
 
0.9%
O 2
 
0.9%
t 2
 
0.9%
Other values (3) 3
 
1.4%

MoSold
Real number (ℝ)

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3219178
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-05-10T00:22:25.440225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median6
Q38
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.7036262
Coefficient of variation (CV)0.42765918
Kurtosis-0.40410934
Mean6.3219178
Median Absolute Deviation (MAD)2
Skewness0.21205299
Sum9230
Variance7.3095947
MonotonicityNot monotonic
2025-05-10T00:22:25.743662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 253
17.3%
7 234
16.0%
5 204
14.0%
4 141
9.7%
8 122
8.4%
3 106
7.3%
10 89
 
6.1%
11 79
 
5.4%
9 63
 
4.3%
12 59
 
4.0%
Other values (2) 110
7.5%
ValueCountFrequency (%)
1 58
 
4.0%
2 52
 
3.6%
3 106
7.3%
4 141
9.7%
5 204
14.0%
6 253
17.3%
7 234
16.0%
8 122
8.4%
9 63
 
4.3%
10 89
 
6.1%
ValueCountFrequency (%)
12 59
 
4.0%
11 79
 
5.4%
10 89
 
6.1%
9 63
 
4.3%
8 122
8.4%
7 234
16.0%
6 253
17.3%
5 204
14.0%
4 141
9.7%
3 106
7.3%

YrSold
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2009
338 
2007
329 
2006
314 
2008
304 
2010
175 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5840
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2008
2nd row2007
3rd row2008
4th row2006
5th row2008

Common Values

ValueCountFrequency (%)
2009 338
23.2%
2007 329
22.5%
2006 314
21.5%
2008 304
20.8%
2010 175
12.0%

Length

2025-05-10T00:22:25.978234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-10T00:22:26.220916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2009 338
23.2%
2007 329
22.5%
2006 314
21.5%
2008 304
20.8%
2010 175
12.0%

Most occurring characters

ValueCountFrequency (%)
0 2920
50.0%
2 1460
25.0%
9 338
 
5.8%
7 329
 
5.6%
6 314
 
5.4%
8 304
 
5.2%
1 175
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5840
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2920
50.0%
2 1460
25.0%
9 338
 
5.8%
7 329
 
5.6%
6 314
 
5.4%
8 304
 
5.2%
1 175
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5840
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2920
50.0%
2 1460
25.0%
9 338
 
5.8%
7 329
 
5.6%
6 314
 
5.4%
8 304
 
5.2%
1 175
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5840
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2920
50.0%
2 1460
25.0%
9 338
 
5.8%
7 329
 
5.6%
6 314
 
5.4%
8 304
 
5.2%
1 175
 
3.0%

SalePrice
Real number (ℝ)

High correlation 

Distinct663
Distinct (%)45.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180921.2
Minimum34900
Maximum755000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-05-10T00:22:26.520414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34900
5-th percentile88000
Q1129975
median163000
Q3214000
95-th percentile326100
Maximum755000
Range720100
Interquartile range (IQR)84025

Descriptive statistics

Standard deviation79442.503
Coefficient of variation (CV)0.43910003
Kurtosis6.5362819
Mean180921.2
Median Absolute Deviation (MAD)38000
Skewness1.8828758
Sum2.6414495 × 108
Variance6.3111113 × 109
MonotonicityNot monotonic
2025-05-10T00:22:27.006397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140000 20
 
1.4%
135000 17
 
1.2%
155000 14
 
1.0%
145000 14
 
1.0%
190000 13
 
0.9%
110000 13
 
0.9%
115000 12
 
0.8%
160000 12
 
0.8%
130000 11
 
0.8%
139000 11
 
0.8%
Other values (653) 1323
90.6%
ValueCountFrequency (%)
34900 1
0.1%
35311 1
0.1%
37900 1
0.1%
39300 1
0.1%
40000 1
0.1%
52000 1
0.1%
52500 1
0.1%
55000 2
0.1%
55993 1
0.1%
58500 1
0.1%
ValueCountFrequency (%)
755000 1
0.1%
745000 1
0.1%
625000 1
0.1%
611657 1
0.1%
582933 1
0.1%
556581 1
0.1%
555000 1
0.1%
538000 1
0.1%
501837 1
0.1%
485000 1
0.1%

Interactions

2025-05-10T00:21:55.311982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:20:42.436168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:20:46.364244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:20:50.875279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:20:56.595837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:01.799298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:06.907043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:12.414504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:17.460879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:21.356571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:25.241138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:28.774218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:32.702291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:36.215927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:39.799352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:43.900554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:47.682952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:51.650480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:55.541297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:20:42.662539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:20:46.579798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:20:51.147765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:20:56.964773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:02.282084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:07.223116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:12.714535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:17.677664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:21.563344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:25.439186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:29.003524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:32.898463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:36.471339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:40.013957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:44.120094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:47.916845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:51.860522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:55.748080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:20:42.872657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:20:46.779115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:20:51.415756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:20:57.325920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:02.582964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:07.544025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:13.027279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:17.894986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:21.775459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:25.651264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:29.218487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:33.101334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:36.667231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:40.226607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:44.351502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:48.122685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:52.072111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:55.959976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:20:43.090533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:20:46.980622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:20:51.665365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:20:57.646173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:02.829093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:07.829278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:13.505364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:18.100006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:21.974429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:25.836268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:29.450402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:33.302395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:36.859433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:40.442672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:44.555476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:48.323963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:52.266056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:56.152282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:20:43.312984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:20:47.176753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:20:51.902604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:20:57.969252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:03.107201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:08.088559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:13.780705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:18.302402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:22.167943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:26.024419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:29.649508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:33.487586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:37.039260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:40.668784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:44.746574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:48.518261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:52.463191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:56.354626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:20:43.515819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:20:47.367298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:20:52.145102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:20:58.220404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:03.368783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:08.348012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:14.059606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:18.521279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:22.355269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:26.206553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:29.840286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:33.690555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:37.216896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:41.054830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:44.950976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:48.715617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:52.650674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:56.581071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:20:43.752491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:20:47.591324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:20:52.381751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:20:58.488568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2025-05-10T00:21:21.144946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:25.021160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:28.567989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:32.491048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:36.011596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:39.582203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:43.668241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:47.464545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:51.434704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-10T00:21:55.105993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2025-05-10T00:22:27.353575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1stFlrSFAlleyBedroomAbvGrBldgTypeBsmtExposureBsmtFinSF1BsmtFinType1BsmtFullBathBsmtQualBsmtUnfSFCentralAirExterCondExterQualExterior1stExterior2ndFenceFireplaceQuFireplacesFoundationFullBathGarageAreaGarageCarsGarageFinishGarageTypeGrLivAreaHalfBathHeatingQCHouseStyleIdKitchenAbvGrKitchenQualLotAreaLotConfigLotFrontageLotShapeMSSubClassMSZoningMasVnrTypeMiscFeatureMoSoldNeighborhoodOverallCondOverallQualRoofStyleSalePriceTotRmsAbvGrdTotalBsmtSFYearBuiltYearRemodAddYrSold
1stFlrSF1.0000.3580.1410.1840.1760.3230.1100.1910.2640.2240.1440.0000.2710.1820.1550.0440.1130.3410.0880.2580.4900.2380.2410.1640.4940.1210.0940.160-0.0010.0410.2510.4440.0570.4280.207-0.2780.1600.1680.0460.0540.241-0.1670.4090.1500.5750.3620.8290.2930.2400.008
Alley0.3581.0000.2050.5420.0560.0000.2220.0000.5140.2880.1420.1160.5860.4250.3730.0000.0000.0000.5330.3710.2610.3430.5380.1330.0670.2620.3210.1320.0000.0000.4581.0000.0000.3400.1540.5090.7170.0001.0000.0000.7560.4450.5090.0000.4760.2500.3220.6190.4870.000
BedroomAbvGr0.1410.2051.0000.3020.101-0.0840.1050.2550.0900.1580.1600.0000.1710.0820.0670.1000.0770.1070.0860.4480.1120.1340.1100.1560.5430.2500.0210.2420.0420.2330.1310.3380.0000.3280.0330.0690.1650.0640.3270.0510.206-0.0040.1220.1400.2350.6680.059-0.035-0.0540.021
BldgType0.1840.5420.3021.0000.0750.0000.0990.1600.1630.1220.2890.1100.1760.1630.1870.0000.0000.1250.1880.1050.1410.1540.1850.1570.0490.2290.1120.1560.0000.4880.1490.0360.0690.3130.0840.8510.1890.0220.3700.0000.4190.1280.1310.0480.0880.1980.1200.2500.1950.000
BsmtExposure0.1760.0560.1010.0751.0000.2340.1990.2000.1950.1270.0760.0000.1550.1240.1410.0000.0440.1300.1280.0990.1720.1570.1760.1380.1200.0700.0810.2300.0000.0360.1350.1450.0680.1150.1010.2010.0750.1130.0000.0000.2700.0960.1810.1380.2060.1020.1880.1780.1510.031
BsmtFinSF10.3230.000-0.0840.0000.2341.0000.2710.3970.241-0.5740.1520.0000.2080.1380.1390.0000.1160.2980.1110.1580.2440.1760.2020.1190.0570.0120.0590.114-0.0130.0000.2090.1720.0450.1540.206-0.1080.0910.1440.000-0.0160.201-0.0110.1330.0960.302-0.0500.4100.1900.0630.000
BsmtFinType10.1100.2220.1050.0990.1990.2711.0000.3380.3330.2950.1750.0610.2920.2160.2230.1170.1640.1120.3150.2260.1540.2120.2580.1580.1080.0540.2020.1610.0000.0330.2760.0130.0590.0520.0510.1820.1360.2540.1980.0100.3130.1710.2310.0560.2070.1030.1320.3510.2630.000
BsmtFullBath0.1910.0000.2550.1600.2000.3970.3381.0000.1020.2650.1060.0000.0710.0890.0830.0740.0720.1120.1030.2640.1370.1150.1140.1400.1360.1520.0600.1650.0000.1290.0950.2110.0000.1550.0940.2090.0710.0790.0000.0000.1890.0000.0660.1250.1400.0630.2040.1440.1220.052
BsmtQual0.2640.5140.0900.1630.1950.2410.3330.1021.0000.1850.2140.1110.4620.3240.3160.1060.2560.1780.4050.3470.3350.4020.4100.2660.2490.1540.2710.2120.0000.0760.4190.0000.0860.1250.1360.2660.1910.2820.0000.0000.5350.3230.5100.1670.4550.1890.2930.5160.3920.000
BsmtUnfSF0.2240.2880.1580.1220.127-0.5740.2950.2650.1851.0000.0600.0260.2510.0940.1020.0000.1590.0870.1700.1870.1090.1790.1560.1050.2530.1220.0980.150-0.0100.0620.1930.0780.0120.1190.039-0.1180.0720.1240.0000.0370.191-0.1280.2730.0910.1850.2610.3290.1390.1770.043
CentralAir0.1440.1420.1600.2890.0760.1520.1750.1060.2140.0601.0000.2000.2780.3490.3300.0540.0630.1960.3650.1030.2760.2830.2430.3030.1580.1300.3790.2330.0000.2450.3430.0000.0630.0520.1080.2540.2970.0000.2210.0000.3820.3150.3740.0550.4180.1120.2230.4380.3780.000
ExterCond0.0000.1160.0000.1100.0000.0000.0610.0000.1110.0260.2001.0000.1820.0950.0670.0730.0000.0340.1230.0750.1140.1290.1040.0660.0530.0510.0620.1030.0320.0000.1780.0000.0000.0000.0000.1150.0790.1070.3680.0000.1530.3790.1950.0920.1050.0000.0380.1890.0990.011
ExterQual0.2710.5860.1710.1760.1550.2080.2920.0710.4620.2510.2780.1821.0000.3510.3550.0910.2070.1850.3710.3180.3440.3610.3820.2760.2860.1510.3240.1760.0000.0880.5460.0000.0140.1380.1120.2430.2390.2480.1250.0320.4850.3190.6140.1460.4760.2740.3190.4350.3890.038
Exterior1st0.1820.4250.0820.1630.1240.1380.2160.0890.3240.0940.3490.0950.3511.0000.7590.0000.2100.1450.3150.2370.1420.2430.3330.2090.1110.1200.2660.1600.0000.1570.2900.0260.0520.1130.0820.1880.1780.2920.0750.0000.2880.1890.2000.1380.1640.0980.1340.3350.2850.041
Exterior2nd0.1550.3730.0670.1870.1410.1390.2230.0830.3160.1020.3300.0670.3550.7591.0000.0000.1740.1180.3140.2250.1390.2350.3350.2160.1230.1660.2650.1670.0000.1300.2840.0710.0760.1290.0930.2100.1860.2680.3700.0050.3170.1690.1930.1600.1750.0990.1440.3260.2770.030
Fence0.0440.0000.1000.0000.0000.0000.1170.0740.1060.0000.0540.0730.0910.0000.0001.0000.0000.0540.0300.1060.1300.1190.0960.1750.0820.1530.0220.1420.0000.0000.1480.1280.0000.0000.1160.1450.0000.0000.0000.0890.1230.0520.2050.0000.1200.0770.0000.0800.1520.000
FireplaceQu0.1130.0000.0770.0000.0440.1160.1640.0720.2560.1590.0630.0000.2070.2100.1740.0001.0000.0580.1410.1290.1630.1800.1160.1000.1820.0720.1390.1180.0000.0390.2280.0000.0410.0000.0000.1200.0530.3040.0000.0200.3050.1220.2520.0670.2030.1220.1450.2840.3040.000
Fireplaces0.3410.0000.1070.1250.1300.2980.1120.1120.1780.0870.1960.0340.1850.1450.1180.0540.0581.0000.1200.1800.2280.2020.2490.2080.3760.1640.0970.0990.0000.0860.1840.1600.0490.2500.1410.1930.1360.0040.0830.0000.3050.1050.2670.0800.2890.2230.3200.1690.1360.030
Foundation0.0880.5330.0860.1880.1280.1110.3150.1030.4050.1700.3650.1230.3710.3150.3140.0300.1410.1201.0000.2850.1890.2700.3760.2370.1520.1640.2930.2160.0000.1670.3430.0000.0430.1150.1170.2630.2240.2360.0000.0000.4170.2560.2910.0920.2580.1180.2330.5020.3220.033
FullBath0.2580.3710.4480.1050.0990.1580.2260.2640.3470.1870.1030.0750.3180.2370.2250.1060.1290.1800.2851.0000.2790.3290.3250.2640.4680.2300.1990.2360.0000.1130.2780.0980.0410.1350.1020.2490.1750.1440.5430.0440.3690.3090.4040.1390.4160.3890.2360.3510.2700.000
GarageArea0.4900.2610.1120.1410.1720.2440.1540.1370.3350.1090.2760.1140.3440.1420.1390.1300.1630.2280.1890.2791.0000.7590.2910.1940.4680.1600.1410.1220.0070.0920.3340.3670.0420.3780.156-0.0470.1890.1990.0000.0330.259-0.2010.5420.0770.6490.3310.4870.5280.3980.000
GarageCars0.2380.3430.1340.1540.1570.1760.2120.1150.4020.1790.2830.1290.3610.2430.2350.1190.1800.2020.2700.3290.7591.0000.3250.2280.2870.1970.1800.1640.0000.1230.3630.0110.0440.1820.1200.2430.1440.2070.0550.0000.3910.2190.4020.1330.4160.2420.2600.3400.2760.000
GarageFinish0.2410.5380.1100.1850.1760.2020.2580.1140.4100.1560.2430.1040.3820.3330.3350.0960.1160.2490.3760.3250.2910.3251.0000.4530.2590.1700.2880.2400.0000.1200.3490.0490.0360.1590.1690.3290.2250.1500.0530.0000.4740.2580.4090.1100.4130.2120.2800.4610.3560.000
GarageType0.1640.1330.1560.1570.1380.1190.1580.1400.2660.1050.3030.0660.2760.2090.2160.1750.1000.2080.2370.2640.1940.2280.4531.0000.1980.2140.1490.2190.0000.1900.2210.0490.0560.1220.1390.2670.2130.2560.0000.0170.3130.1820.2130.0670.2270.1760.1700.2850.2100.000
GrLivArea0.4940.0670.5430.0490.1200.0570.1080.1360.2490.2530.1580.0530.2860.1110.1230.0820.1820.3760.1520.4680.4680.2870.2590.1981.0000.3000.1430.2580.0030.0000.2660.4490.0460.3760.2220.2040.1060.0940.3210.0810.209-0.1540.6030.0620.7310.8280.3710.2880.2820.042
HalfBath0.1210.2620.2500.2290.0700.0120.0540.1520.1540.1220.1300.0510.1510.1200.1660.1530.0720.1640.1640.2300.1600.1970.1700.2140.3001.0000.0970.4610.0000.1920.1480.0000.0000.0330.0840.5120.1400.1010.4820.0550.3000.0790.2250.2100.2080.2700.0970.2270.2000.000
HeatingQC0.0940.3210.0210.1120.0810.0590.2020.0600.2710.0980.3790.0620.3240.2660.2650.0220.1390.0970.2930.1990.1410.1800.2880.1490.1430.0971.0000.1680.0000.0960.3180.0000.0100.0490.0530.1680.1170.2190.0800.0170.2960.1780.2590.0000.2380.0990.1400.3360.3280.004
HouseStyle0.1600.1320.2420.1560.2300.1140.1610.1650.2120.1500.2330.1030.1760.1600.1670.1420.1180.0990.2160.2360.1220.1640.2400.2190.2580.4610.1681.0000.0190.1500.1470.0000.0000.0440.0730.6170.1840.1390.0000.0000.2940.1220.1440.1020.1290.2660.1640.2910.2000.000
Id-0.0010.0000.0420.0000.000-0.0130.0000.0000.000-0.0100.0000.0320.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.0030.0000.0000.0191.0000.0380.000-0.0050.000-0.0330.0130.0190.0000.0390.1850.0190.0000.004-0.0290.049-0.0190.026-0.033-0.005-0.0120.018
KitchenAbvGr0.0410.0000.2330.4880.0360.0000.0330.1290.0760.0620.2450.0000.0880.1570.1300.0000.0390.0860.1670.1130.0920.1230.1200.1900.0000.1920.0960.1500.0381.0000.1020.0000.0410.0190.0370.4760.0910.0000.3980.0200.1020.0730.1060.1600.0460.1740.0700.2140.1140.000
KitchenQual0.2510.4580.1310.1490.1350.2090.2760.0950.4190.1930.3430.1780.5460.2900.2840.1480.2280.1840.3430.2780.3340.3630.3490.2210.2660.1480.3180.1470.0000.1021.0000.0000.0000.1070.0920.2260.1740.2580.1910.0310.4440.2470.5400.1110.4620.2380.2960.4020.4170.000
LotArea0.4441.0000.3380.0360.1450.1720.0130.2110.0000.0780.0000.0000.0000.0260.0710.1280.0000.1600.0000.0980.3670.0110.0490.0490.4490.0000.0000.000-0.0050.0000.0001.0000.0790.6500.266-0.2700.0000.1830.0000.0060.162-0.0470.2330.1120.4560.4060.3660.1030.0750.000
LotConfig0.0570.0000.0000.0690.0680.0450.0590.0000.0860.0120.0630.0000.0140.0520.0760.0000.0410.0490.0430.0410.0420.0440.0360.0560.0460.0000.0100.0000.0000.0410.0000.0791.0000.1650.2210.0620.0640.0000.0420.0320.1370.0000.0170.0750.0870.0000.0290.1050.0860.031
LotFrontage0.4280.3400.3280.3130.1150.1540.0520.1550.1250.1190.0520.0000.1380.1130.1290.0000.0000.2500.1150.1350.3780.1820.1590.1220.3760.0330.0490.044-0.0330.0190.1070.6500.1651.0000.297-0.3140.1930.0360.0000.0260.245-0.0830.2550.1540.4090.3660.3860.1950.1170.007
LotShape0.2070.1540.0330.0840.1010.2060.0510.0940.1360.0390.1080.0000.1120.0820.0930.1160.0000.1410.1170.1020.1560.1200.1690.1390.2220.0840.0530.0730.0130.0370.0920.2660.2210.2971.0000.1380.1520.0570.0680.0000.2440.0600.1160.0350.1970.0900.2000.1740.1390.000
MSSubClass-0.2780.5090.0690.8510.201-0.1080.1820.2090.266-0.1180.2540.1150.2430.1880.2100.1450.1200.1930.2630.249-0.0470.2430.3290.2670.2040.5120.1680.6170.0190.4760.226-0.2700.062-0.3140.1381.0000.2640.1400.2780.0180.422-0.0720.1080.1170.0070.166-0.3190.0360.0070.000
MSZoning0.1600.7170.1650.1890.0750.0910.1360.0710.1910.0720.2970.0790.2390.1780.1860.0000.0530.1360.2240.1750.1890.1440.2250.2130.1060.1400.1170.1840.0000.0910.1740.0000.0640.1930.1520.2641.0000.0590.0000.0230.6410.1610.1900.0730.2060.1750.1190.2950.2020.000
MasVnrType0.1680.0000.0640.0220.1130.1440.2540.0790.2820.1240.0000.1070.2480.2920.2680.0000.3040.0040.2360.1440.1990.2070.1500.2560.0940.1010.2190.1390.0390.0000.2580.1830.0000.0360.0570.1400.0591.0000.0000.0000.3860.1790.2710.1020.2110.0860.1770.3120.3730.066
MiscFeature0.0461.0000.3270.3700.0000.0000.1980.0000.0000.0000.2210.3680.1250.0750.3700.0000.0000.0830.0000.5430.0000.0550.0530.0000.3210.4820.0800.0000.1850.3980.1910.0000.0420.0000.0680.2780.0000.0001.0000.0000.0000.0000.0000.3730.0000.4240.1970.0000.0000.142
MoSold0.0540.0000.0510.0000.000-0.0160.0100.0000.0000.0370.0000.0000.0320.0000.0050.0890.0200.0000.0000.0440.0330.0000.0000.0170.0810.0550.0170.0000.0190.0200.0310.0060.0320.0260.0000.0180.0230.0000.0001.0000.052-0.0070.0610.0000.0690.0400.0300.0190.0210.155
Neighborhood0.2410.7560.2060.4190.2700.2010.3130.1890.5350.1910.3820.1530.4850.2880.3170.1230.3050.3050.4170.3690.2590.3910.4740.3130.2090.3000.2960.2940.0000.1020.4440.1620.1370.2450.2440.4220.6410.3860.0000.0521.0000.2220.3210.1860.3190.2040.2370.4800.3880.000
OverallCond-0.1670.445-0.0040.1280.096-0.0110.1710.0000.323-0.1280.3150.3790.3190.1890.1690.0520.1220.1050.2560.309-0.2010.2190.2580.182-0.1540.0790.1780.1220.0040.0730.247-0.0470.000-0.0830.060-0.0720.1610.1790.000-0.0070.2221.000-0.1780.044-0.129-0.105-0.217-0.417-0.0410.050
OverallQual0.4090.5090.1220.1310.1810.1330.2310.0660.5100.2730.3740.1950.6140.2000.1930.2050.2520.2670.2910.4040.5420.4020.4090.2130.6030.2250.2590.144-0.0290.1060.5400.2330.0170.2550.1160.1080.1900.2710.0000.0610.321-0.1781.0000.1170.8100.4280.4600.6470.5580.000
RoofStyle0.1500.0000.1400.0480.1380.0960.0560.1250.1670.0910.0550.0920.1460.1380.1600.0000.0670.0800.0920.1390.0770.1330.1100.0670.0620.2100.0000.1020.0490.1600.1110.1120.0750.1540.0350.1170.0730.1020.3730.0000.1860.0440.1171.0000.1130.1220.1230.1600.0810.000
SalePrice0.5750.4760.2350.0880.2060.3020.2070.1400.4550.1850.4180.1050.4760.1640.1750.1200.2030.2890.2580.4160.6490.4160.4130.2270.7310.2080.2380.129-0.0190.0460.4620.4560.0870.4090.1970.0070.2060.2110.0000.0690.319-0.1290.8100.1131.0000.5330.6030.6530.5710.000
TotRmsAbvGrd0.3620.2500.6680.1980.102-0.0500.1030.0630.1890.2610.1120.0000.2740.0980.0990.0770.1220.2230.1180.3890.3310.2420.2120.1760.8280.2700.0990.2660.0260.1740.2380.4060.0000.3660.0900.1660.1750.0860.4240.0400.204-0.1050.4280.1220.5331.0000.2340.1770.1980.000
TotalBsmtSF0.8290.3220.0590.1200.1880.4100.1320.2040.2930.3290.2230.0380.3190.1340.1440.0000.1450.3200.2330.2360.4870.2600.2800.1700.3710.0970.1400.164-0.0330.0700.2960.3660.0290.3860.200-0.3190.1190.1770.1970.0300.237-0.2170.4600.1230.6030.2341.0000.4270.2990.000
YearBuilt0.2930.619-0.0350.2500.1780.1900.3510.1440.5160.1390.4380.1890.4350.3350.3260.0800.2840.1690.5020.3510.5280.3400.4610.2850.2880.2270.3360.291-0.0050.2140.4020.1030.1050.1950.1740.0360.2950.3120.0000.0190.480-0.4170.6470.1600.6530.1770.4271.0000.6840.000
YearRemodAdd0.2400.487-0.0540.1950.1510.0630.2630.1220.3920.1770.3780.0990.3890.2850.2770.1520.3040.1360.3220.2700.3980.2760.3560.2100.2820.2000.3280.200-0.0120.1140.4170.0750.0860.1170.1390.0070.2020.3730.0000.0210.388-0.0410.5580.0810.5710.1980.2990.6841.0000.000
YrSold0.0080.0000.0210.0000.0310.0000.0000.0520.0000.0430.0000.0110.0380.0410.0300.0000.0000.0300.0330.0000.0000.0000.0000.0000.0420.0000.0040.0000.0180.0000.0000.0000.0310.0070.0000.0000.0000.0660.1420.1550.0000.0500.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2025-05-10T00:21:59.582247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-10T00:22:00.820374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-05-10T00:22:01.453289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

IdMSSubClassMSZoningLotFrontageLotAreaAlleyLotShapeLotConfigNeighborhoodBldgTypeHouseStyleOverallQualOverallCondYearBuiltYearRemodAddRoofStyleExterior1stExterior2ndMasVnrTypeExterQualExterCondFoundationBsmtQualBsmtExposureBsmtFinType1BsmtFinSF1BsmtUnfSFTotalBsmtSFHeatingQCCentralAir1stFlrSFGrLivAreaBsmtFullBathFullBathHalfBathBedroomAbvGrKitchenAbvGrKitchenQualTotRmsAbvGrdFireplacesFireplaceQuGarageTypeGarageFinishGarageCarsGarageAreaFenceMiscFeatureMoSoldYrSoldSalePrice
0160RL65.08450NaNRegInsideCollgCr1Fam2Story7520032003GableVinylSdVinylSdBrkFaceGdTAPConcGdNoGLQ706150856ExY856171012131Gd80NaNAttchdRFn2548NaNNaN22008208500
1220RL80.09600NaNRegFR2Veenker1Fam1Story6819761976GableMetalSdMetalSdNaNTATACBlockGdGdALQ9782841262ExY1262126202031TA61TAAttchdRFn2460NaNNaN52007181500
2360RL68.011250NaNIR1InsideCollgCr1Fam2Story7520012002GableVinylSdVinylSdBrkFaceGdTAPConcGdMnGLQ486434920ExY920178612131Gd61TAAttchdRFn2608NaNNaN92008223500
3470RL60.09550NaNIR1CornerCrawfor1Fam2Story7519151970GableWd SdngWd ShngNaNTATABrkTilTANoALQ216540756GdY961171711031Gd71GdDetchdUnf3642NaNNaN22006140000
4560RL84.014260NaNIR1FR2NoRidge1Fam2Story8520002000GableVinylSdVinylSdBrkFaceGdTAPConcGdAvGLQ6554901145ExY1145219812141Gd91TAAttchdRFn3836NaNNaN122008250000
5650RL85.014115NaNIR1InsideMitchel1Fam1.5Fin5519931995GableVinylSdVinylSdNaNTATAWoodGdNoGLQ73264796ExY796136211111TA50NaNAttchdUnf2480MnPrvShed102009143000
6720RL75.010084NaNRegInsideSomerst1Fam1Story8520042005GableVinylSdVinylSdStoneGdTAPConcExAvGLQ13693171686ExY1694169412031Gd71GdAttchdRFn2636NaNNaN82007307000
7860RLNaN10382NaNIR1CornerNWAmes1Fam2Story7619731973GableHdBoardHdBoardStoneTATACBlockGdMnALQ8592161107ExY1107209012131TA72TAAttchdRFn2484NaNShed112009200000
8950RM51.06120NaNRegInsideOldTown1Fam1.5Fin7519311950GableBrkFaceWd ShngNaNTATABrkTilTANoUnf0952952GdY1022177402022TA82TADetchdUnf2468NaNNaN42008129900
910190RL50.07420NaNRegCornerBrkSide2fmCon1.5Unf5619391950GableMetalSdMetalSdNaNTATABrkTilTANoGLQ851140991ExY1077107711022TA52TAAttchdRFn1205NaNNaN12008118000
IdMSSubClassMSZoningLotFrontageLotAreaAlleyLotShapeLotConfigNeighborhoodBldgTypeHouseStyleOverallQualOverallCondYearBuiltYearRemodAddRoofStyleExterior1stExterior2ndMasVnrTypeExterQualExterCondFoundationBsmtQualBsmtExposureBsmtFinType1BsmtFinSF1BsmtUnfSFTotalBsmtSFHeatingQCCentralAir1stFlrSFGrLivAreaBsmtFullBathFullBathHalfBathBedroomAbvGrKitchenAbvGrKitchenQualTotRmsAbvGrdFireplacesFireplaceQuGarageTypeGarageFinishGarageCarsGarageAreaFenceMiscFeatureMoSoldYrSoldSalePrice
1450145190RL60.09000NaNRegFR2NAmesDuplex2Story5519741974GableVinylSdVinylSdNaNTATACBlockGdNoUnf0896896TAY896179202242TA80NaNNaNNaN00NaNNaN92009136000
1451145220RL78.09262NaNRegInsideSomerst1Fam1Story8520082009GableCemntBdCmentBdStoneGdTAPConcGdNoUnf015731573ExY1578157802031Ex71GdAttchdFin3840NaNNaN52009287090
14521453180RM35.03675NaNRegInsideEdwardsTwnhsESLvl5520052005GableVinylSdVinylSdBrkFaceTATAPConcGdGdGLQ5470547GdY1072107211021TA50NaNBasmentFin2525NaNNaN52006145000
1453145420RL90.017217NaNRegInsideMitchel1Fam1Story5520062006GableVinylSdVinylSdNaNTATAPConcGdNoUnf011401140ExY1140114001031TA60NaNNaNNaN00NaNNaN7200684500
1454145520FV62.07500PaveRegInsideSomerst1Fam1Story7520042005GableVinylSdVinylSdNaNGdTAPConcGdNoGLQ4108111221ExY1221122112021Gd60NaNAttchdRFn2400NaNNaN102009185000
1455145660RL62.07917NaNRegInsideGilbert1Fam2Story6519992000GableVinylSdVinylSdNaNTATAPConcGdNoUnf0953953ExY953164702131TA71TAAttchdRFn2460NaNNaN82007175000
1456145720RL85.013175NaNRegInsideNWAmes1Fam1Story6619781988GablePlywoodPlywoodStoneTATACBlockGdNoALQ7905891542TAY2073207312031TA72TAAttchdUnf2500MnPrvNaN22010210000
1457145870RL66.09042NaNRegInsideCrawfor1Fam2Story7919412006GableCemntBdCmentBdNaNExGdStoneTANoGLQ2758771152ExY1188234002041Gd92GdAttchdRFn1252GdPrvShed52010266500
1458145920RL68.09717NaNRegInsideNAmes1Fam1Story5619501996HipMetalSdMetalSdNaNTATACBlockTAMnGLQ4901078GdY1078107811021Gd50NaNAttchdUnf1240NaNNaN42010142125
1459146020RL75.09937NaNRegInsideEdwards1Fam1Story5619651965GableHdBoardHdBoardNaNGdTACBlockTANoBLQ8301361256GdY1256125611131TA60NaNAttchdFin1276NaNNaN62008147500